{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "598b6054-6147-44b1-a3f2-35ae1f7828ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import pandas as pd\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "from statsmodels.sandbox.stats.multicomp import multipletests\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from skbio.stats.composition import clr\n",
    "from matplotlib_venn import venn3\n",
    "\n",
    "sns.set_context('talk')\n",
    "sns.set(rc={\"figure.dpi\":150, 'savefig.dpi':300})\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "def p_adjust(pvalues, method='fdr_bh'):\n",
    "    res = multipletests(pvalues, method=method)\n",
    "    return np.array(res[1], dtype=float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4d9bb0-fa06-4898-aca1-d72c51431e2c",
   "metadata": {},
   "source": [
    "# Metabolomics correlations\n",
    "##### 7/18/22\n",
    "##### Michael Shaffer\n",
    "##### Merck ESC, Sys bio group\n",
    "\n",
    "After some success with correlating KOs with continuous titer we decided to use the same approach on the metabolomics data. This data table came from Tom directly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396ba13c-ce03-4289-8e8a-5a1ebb4e0f3e",
   "metadata": {},
   "source": [
    "## Read in data\n",
    "\n",
    "Read in the excel table from Tom and fill out empty cells with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae77ebe4-ab21-4f4b-b6f7-7023b8acf0a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GCDCA</th>\n",
       "      <th>GDCA</th>\n",
       "      <th>GHDCA or GUDCA</th>\n",
       "      <th>CA</th>\n",
       "      <th>TCDCA</th>\n",
       "      <th>TCA</th>\n",
       "      <th>CDCA</th>\n",
       "      <th>7oxoCA</th>\n",
       "      <th>TUDCA</th>\n",
       "      <th>UDCA or HDCA</th>\n",
       "      <th>...</th>\n",
       "      <th>Uracil</th>\n",
       "      <th>Adenosine</th>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <th>Vanillic acid</th>\n",
       "      <th>Thymine</th>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <th>Homocitrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compound name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P103_V12_05052020</th>\n",
       "      <td>13165.576990</td>\n",
       "      <td>6486.205847</td>\n",
       "      <td>1037.427698</td>\n",
       "      <td>877.477368</td>\n",
       "      <td>842.441112</td>\n",
       "      <td>428.399288</td>\n",
       "      <td>412.663454</td>\n",
       "      <td>6.309238</td>\n",
       "      <td>26.103778</td>\n",
       "      <td>37.617317</td>\n",
       "      <td>...</td>\n",
       "      <td>571</td>\n",
       "      <td>112</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>426</td>\n",
       "      <td>1223</td>\n",
       "      <td>6865</td>\n",
       "      <td>549</td>\n",
       "      <td>217</td>\n",
       "      <td>3044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>5324.081005</td>\n",
       "      <td>615.267690</td>\n",
       "      <td>1431.812977</td>\n",
       "      <td>536.910350</td>\n",
       "      <td>477.807838</td>\n",
       "      <td>761.254736</td>\n",
       "      <td>55.773899</td>\n",
       "      <td>4.142229</td>\n",
       "      <td>91.815489</td>\n",
       "      <td>19.861706</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>368</td>\n",
       "      <td>206</td>\n",
       "      <td>217</td>\n",
       "      <td>201</td>\n",
       "      <td>372</td>\n",
       "      <td>53</td>\n",
       "      <td>230</td>\n",
       "      <td>216</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>11967.195030</td>\n",
       "      <td>3.048386</td>\n",
       "      <td>8876.579286</td>\n",
       "      <td>884.309058</td>\n",
       "      <td>2720.613568</td>\n",
       "      <td>3492.197205</td>\n",
       "      <td>106.185518</td>\n",
       "      <td>7.807234</td>\n",
       "      <td>664.956760</td>\n",
       "      <td>113.460800</td>\n",
       "      <td>...</td>\n",
       "      <td>594</td>\n",
       "      <td>112</td>\n",
       "      <td>8</td>\n",
       "      <td>330</td>\n",
       "      <td>102</td>\n",
       "      <td>9</td>\n",
       "      <td>87</td>\n",
       "      <td>55</td>\n",
       "      <td>177</td>\n",
       "      <td>6952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>117.128627</td>\n",
       "      <td>-0.240781</td>\n",
       "      <td>116.125243</td>\n",
       "      <td>29.790592</td>\n",
       "      <td>6.916959</td>\n",
       "      <td>5.775257</td>\n",
       "      <td>1.574994</td>\n",
       "      <td>0.418815</td>\n",
       "      <td>1.671431</td>\n",
       "      <td>4.996911</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>226</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>249</td>\n",
       "      <td>104</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>16651.224940</td>\n",
       "      <td>1707.969274</td>\n",
       "      <td>3575.590198</td>\n",
       "      <td>1277.281488</td>\n",
       "      <td>2356.663104</td>\n",
       "      <td>1109.351448</td>\n",
       "      <td>204.781749</td>\n",
       "      <td>41.696046</td>\n",
       "      <td>216.707453</td>\n",
       "      <td>107.961759</td>\n",
       "      <td>...</td>\n",
       "      <td>375</td>\n",
       "      <td>184</td>\n",
       "      <td>205</td>\n",
       "      <td>141</td>\n",
       "      <td>253</td>\n",
       "      <td>702</td>\n",
       "      <td>298</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>3468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          GCDCA         GDCA  GHDCA or GUDCA           CA  \\\n",
       "Compound name                                                               \n",
       "P103_V12_05052020  13165.576990  6486.205847     1037.427698   877.477368   \n",
       "P106_V9_04022019    5324.081005   615.267690     1431.812977   536.910350   \n",
       "P107_A1_04152019   11967.195030     3.048386     8876.579286   884.309058   \n",
       "P108_V9_04022019     117.128627    -0.240781      116.125243    29.790592   \n",
       "P108_V12_05212020  16651.224940  1707.969274     3575.590198  1277.281488   \n",
       "\n",
       "                         TCDCA          TCA        CDCA     7oxoCA  \\\n",
       "Compound name                                                        \n",
       "P103_V12_05052020   842.441112   428.399288  412.663454   6.309238   \n",
       "P106_V9_04022019    477.807838   761.254736   55.773899   4.142229   \n",
       "P107_A1_04152019   2720.613568  3492.197205  106.185518   7.807234   \n",
       "P108_V9_04022019      6.916959     5.775257    1.574994   0.418815   \n",
       "P108_V12_05212020  2356.663104  1109.351448  204.781749  41.696046   \n",
       "\n",
       "                        TUDCA  UDCA or HDCA  ...  Uracil  Adenosine  \\\n",
       "Compound name                                ...                      \n",
       "P103_V12_05052020   26.103778     37.617317  ...     571        112   \n",
       "P106_V9_04022019    91.815489     19.861706  ...     400        368   \n",
       "P107_A1_04152019   664.956760    113.460800  ...     594        112   \n",
       "P108_V9_04022019     1.671431      4.996911  ...      60        226   \n",
       "P108_V12_05212020  216.707453    107.961759  ...     375        184   \n",
       "\n",
       "                   Phenaceturic acid  N-Acetyl D-galactosamine  \\\n",
       "Compound name                                                    \n",
       "P103_V12_05052020                 45                        90   \n",
       "P106_V9_04022019                 206                       217   \n",
       "P107_A1_04152019                   8                       330   \n",
       "P108_V9_04022019                  11                        16   \n",
       "P108_V12_05212020                205                       141   \n",
       "\n",
       "                   Pyridoxal hydrochloride  2-Deoxyuridine  Vanillic acid  \\\n",
       "Compound name                                                               \n",
       "P103_V12_05052020                      426            1223           6865   \n",
       "P106_V9_04022019                       201             372             53   \n",
       "P107_A1_04152019                       102               9             87   \n",
       "P108_V9_04022019                       249             104             64   \n",
       "P108_V12_05212020                      253             702            298   \n",
       "\n",
       "                   Thymine  Nicotinic acid  Homocitrate  \n",
       "Compound name                                            \n",
       "P103_V12_05052020      549             217         3044  \n",
       "P106_V9_04022019       230             216          109  \n",
       "P107_A1_04152019        55             177         6952  \n",
       "P108_V9_04022019        18              24           36  \n",
       "P108_V12_05212020      196             196         3468  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data = pd.read_excel('../../data/metabolomics_abunds.xlsx', index_col=0).fillna(0)\n",
    "metab_data = metab_data.drop('2-Methyl-1-butanol', axis=1) # drop 2-methyl-1-butanol\n",
    "metab_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fcdc66f-562b-44ef-aa90-68f9a2a511f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(metab_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1beeb59b-e8eb-4320-8ead-baff4dc6d0de",
   "metadata": {},
   "source": [
    "111 total compounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e25e7c30-c055-461b-94fc-cf83819b00cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(metab_data < 0).sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bf9ccc-21da-4366-9d65-c42295ef8d3d",
   "metadata": {},
   "source": [
    "There are only 34 negative values in the data set so not much to worry about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afa2e02b-9851-41bb-be46-7da0a98b70f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        Compound name    \n",
       "GDCA    P108_V9_04022019    -0.240781\n",
       "        P134_V9_12062019    -3.462650\n",
       "        P136_V9_01092020    -3.398780\n",
       "        P136_V12_01042021   -0.059861\n",
       "        P202_V12_02252020   -1.708202\n",
       "        P212_V5_05232018    -2.539283\n",
       "        P215_V9_03292019    -4.984036\n",
       "        P215_V12_03302020   -2.424750\n",
       "        P217_V5_06122018    -1.613075\n",
       "        P217_V9_04122019    -0.517431\n",
       "        P218_V5_06062018    -0.427326\n",
       "        P220_V5_06132018    -0.218075\n",
       "        P224_V9_05292019    -0.396865\n",
       "        P229_V5_07022018    -0.664653\n",
       "        P235_V5_08152018    -0.203266\n",
       "        P237_V9_06242019    -0.407863\n",
       "        P239_V5_08292018    -0.942639\n",
       "        P246_V10_10032019   -1.541833\n",
       "        P250_V5_11062018    -1.424605\n",
       "        P250_V9_09092019    -2.380330\n",
       "        P252_V9_09102019    -1.033160\n",
       "        P256_V9_10082018    -0.539685\n",
       "        P258_V5_01032019    -1.193836\n",
       "        P260_V9_11072019    -7.410659\n",
       "        P261_V5_01032019    -2.489533\n",
       "        P263_V9_11262019    -2.335442\n",
       "7oxoCA  P253_V12_09242020   -2.383277\n",
       "DCA     P115_V12_05282020   -0.036132\n",
       "        P136_V9_01092020    -0.927228\n",
       "        P233_V5_07182018    -0.085570\n",
       "        P234_V5_08072018    -1.335333\n",
       "        P236_V5_08142018    -2.929766\n",
       "        P238_V9_06242019    -0.044959\n",
       "        P252_V9_09102019    -1.587543\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data.unstack()[metab_data.unstack() < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c1a43f-663f-4866-b3ef-cd701aa1ed47",
   "metadata": {},
   "source": [
    "There are negative numbers in the data set. Checked with Tom and he said that these can be treated as zeros. So do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a69bd109-0485-4dd4-9b7b-b39fab00e911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace negative numbers with 0 as Tom said in an email\n",
    "metab_data[metab_data < 0] = 0\n",
    "(metab_data == 0).sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6925d1d5-9370-4855-af1b-94b3a63700e7",
   "metadata": {},
   "source": [
    "Relative abundance normalized version of table is calculated by summing rows and dividing column values by totals. Makes it so that values are percent abundances of sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "758d39b1-abdb-407b-b02c-c0792f1d0d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GCDCA</th>\n",
       "      <th>GDCA</th>\n",
       "      <th>GHDCA or GUDCA</th>\n",
       "      <th>CA</th>\n",
       "      <th>TCDCA</th>\n",
       "      <th>TCA</th>\n",
       "      <th>CDCA</th>\n",
       "      <th>7oxoCA</th>\n",
       "      <th>TUDCA</th>\n",
       "      <th>UDCA or HDCA</th>\n",
       "      <th>...</th>\n",
       "      <th>Uracil</th>\n",
       "      <th>Adenosine</th>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <th>Vanillic acid</th>\n",
       "      <th>Thymine</th>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <th>Homocitrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compound name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>P103_V12_05052020</th>\n",
       "      <td>0.001680</td>\n",
       "      <td>8.275956e-04</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>5.265304e-05</td>\n",
       "      <td>8.050157e-07</td>\n",
       "      <td>3.330664e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000876</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>0.000847</td>\n",
       "      <td>9.783810e-05</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>8.869005e-06</td>\n",
       "      <td>6.586853e-07</td>\n",
       "      <td>1.460024e-05</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>0.002120</td>\n",
       "      <td>5.401497e-07</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>1.881523e-05</td>\n",
       "      <td>1.383380e-06</td>\n",
       "      <td>1.178250e-04</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>8.751132e-07</td>\n",
       "      <td>2.327061e-07</td>\n",
       "      <td>9.286966e-07</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>0.002614</td>\n",
       "      <td>2.681638e-04</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>3.215225e-05</td>\n",
       "      <td>6.546587e-06</td>\n",
       "      <td>3.402467e-05</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GCDCA          GDCA  GHDCA or GUDCA        CA     TCDCA  \\\n",
       "Compound name                                                                   \n",
       "P103_V12_05052020  0.001680  8.275956e-04        0.000132  0.000112  0.000107   \n",
       "P106_V9_04022019   0.000847  9.783810e-05        0.000228  0.000085  0.000076   \n",
       "P107_A1_04152019   0.002120  5.401497e-07        0.001573  0.000157  0.000482   \n",
       "P108_V9_04022019   0.000065  0.000000e+00        0.000065  0.000017  0.000004   \n",
       "P108_V12_05212020  0.002614  2.681638e-04        0.000561  0.000201  0.000370   \n",
       "\n",
       "                        TCA          CDCA        7oxoCA         TUDCA  \\\n",
       "Compound name                                                           \n",
       "P103_V12_05052020  0.000055  5.265304e-05  8.050157e-07  3.330664e-06   \n",
       "P106_V9_04022019   0.000121  8.869005e-06  6.586853e-07  1.460024e-05   \n",
       "P107_A1_04152019   0.000619  1.881523e-05  1.383380e-06  1.178250e-04   \n",
       "P108_V9_04022019   0.000003  8.751132e-07  2.327061e-07  9.286966e-07   \n",
       "P108_V12_05212020  0.000174  3.215225e-05  6.546587e-06  3.402467e-05   \n",
       "\n",
       "                   UDCA or HDCA  ...    Uracil  Adenosine  Phenaceturic acid  \\\n",
       "Compound name                    ...                                           \n",
       "P103_V12_05052020      0.000005  ...  0.000073   0.000014           0.000006   \n",
       "P106_V9_04022019       0.000003  ...  0.000064   0.000059           0.000033   \n",
       "P107_A1_04152019       0.000020  ...  0.000105   0.000020           0.000001   \n",
       "P108_V9_04022019       0.000003  ...  0.000033   0.000126           0.000006   \n",
       "P108_V12_05212020      0.000017  ...  0.000059   0.000029           0.000032   \n",
       "\n",
       "                   N-Acetyl D-galactosamine  Pyridoxal hydrochloride  \\\n",
       "Compound name                                                          \n",
       "P103_V12_05052020                  0.000011                 0.000054   \n",
       "P106_V9_04022019                   0.000035                 0.000032   \n",
       "P107_A1_04152019                   0.000058                 0.000018   \n",
       "P108_V9_04022019                   0.000009                 0.000138   \n",
       "P108_V12_05212020                  0.000022                 0.000040   \n",
       "\n",
       "                   2-Deoxyuridine  Vanillic acid   Thymine  Nicotinic acid  \\\n",
       "Compound name                                                                \n",
       "P103_V12_05052020        0.000156       0.000876  0.000070        0.000028   \n",
       "P106_V9_04022019         0.000059       0.000008  0.000037        0.000034   \n",
       "P107_A1_04152019         0.000002       0.000015  0.000010        0.000031   \n",
       "P108_V9_04022019         0.000058       0.000036  0.000010        0.000013   \n",
       "P108_V12_05212020        0.000110       0.000047  0.000031        0.000031   \n",
       "\n",
       "                   Homocitrate  \n",
       "Compound name                   \n",
       "P103_V12_05052020     0.000388  \n",
       "P106_V9_04022019      0.000017  \n",
       "P107_A1_04152019      0.001232  \n",
       "P108_V9_04022019      0.000020  \n",
       "P108_V12_05212020     0.000545  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_rel = metab_data.div(metab_data.sum(axis=1), axis=0)\n",
    "metab_data_rel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcaeb76-e9a5-4c12-93b9-3d8c26568a4c",
   "metadata": {},
   "source": [
    "CLR normalized version of table is calculated using CLR (centered log ratio transformation) which controls for compositionality. This is done using the scikit-bio package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0464f150-61f1-49ff-b44b-2f7e29552f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "metab_data_clr = pd.DataFrame(clr(metab_data + .001), index=metab_data.index, columns=metab_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d27a752-cf90-4bdf-b80d-81f5e723401e",
   "metadata": {},
   "source": [
    "Build simple metadata table based on the sample names from the metabolomics table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63e7c570-f966-4e62-a8ec-6638352c0044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SampleID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P103_V12_05052020</th>\n",
       "      <td>103</td>\n",
       "      <td>V12</td>\n",
       "      <td>05052020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>106</td>\n",
       "      <td>V9</td>\n",
       "      <td>04022019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>107</td>\n",
       "      <td>A1</td>\n",
       "      <td>04152019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>108</td>\n",
       "      <td>V9</td>\n",
       "      <td>04022019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>108</td>\n",
       "      <td>V12</td>\n",
       "      <td>05212020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BabyN VisitCode VisitDate\n",
       "SampleID                                    \n",
       "P103_V12_05052020    103       V12  05052020\n",
       "P106_V9_04022019     106        V9  04022019\n",
       "P107_A1_04152019     107        A1  04152019\n",
       "P108_V9_04022019     108        V9  04022019\n",
       "P108_V12_05212020    108       V12  05212020"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_rows = [[i, int(i.split('_')[0].strip('P')), i.split('_')[1], i.split('_')[-1]] for i in metab_data.index]\n",
    "meta_base = pd.DataFrame(meta_rows, columns=['SampleID', 'BabyN', 'VisitCode', 'VisitDate']).set_index('SampleID')\n",
    "meta_base.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949d7209-89e7-4db9-afc6-1ccc4c674bf4",
   "metadata": {},
   "source": [
    "Connect sample data to 1 yr titer data via the baby numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5b53321-c5d2-4289-9b6c-3e9a6a4f7fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PT</th>\n",
       "      <th>Dip</th>\n",
       "      <th>FHA</th>\n",
       "      <th>PRN</th>\n",
       "      <th>TET</th>\n",
       "      <th>PRP (Hib)</th>\n",
       "      <th>PCV ST1</th>\n",
       "      <th>PCV ST3</th>\n",
       "      <th>PCV ST4</th>\n",
       "      <th>PCV ST5</th>\n",
       "      <th>...</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>protectNorm_Dip</th>\n",
       "      <th>protectNorm_TET</th>\n",
       "      <th>protectNorm_PRP (Hib)</th>\n",
       "      <th>protectNorm_PT</th>\n",
       "      <th>protectNorm_PRN</th>\n",
       "      <th>protectNorm_FHA</th>\n",
       "      <th>geommean_protectNorm</th>\n",
       "      <th>VR_group_v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.39</td>\n",
       "      <td>141.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061955</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>1.3750</td>\n",
       "      <td>1.140388</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958142</td>\n",
       "      <td>0.114018</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>1.1250</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>1.783418</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.449420</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>7.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486810</td>\n",
       "      <td>0.763049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.5</td>\n",
       "      <td>46.800000</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.383505</td>\n",
       "      <td>HVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245121</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>2.4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7500</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>3.440894</td>\n",
       "      <td>HVR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PT   Dip   FHA   PRN   TET  PRP (Hib)  PCV ST1  PCV ST3  PCV ST4  \\\n",
       "106   2.5  0.21  11.0   2.5  0.30       0.39    141.0     35.0     56.0   \n",
       "107   2.5  0.44   3.0   9.0  0.52       1.60   2430.0    415.0    194.0   \n",
       "108   2.5  0.05   1.5   2.5  0.05       0.27     21.0      3.0     24.0   \n",
       "109  27.0   NaN   NaN  63.0  1.35       7.02      NaN      NaN      NaN   \n",
       "110  14.0  0.24  15.0  20.0  2.45        NaN    301.0     63.0    400.0   \n",
       "\n",
       "     PCV ST5  ...  median_mmNorm_PCV  median_mmNorm_DTAPHib  protectNorm_Dip  \\\n",
       "106    139.0  ...           0.061955               0.052874              2.1   \n",
       "107    332.0  ...           0.958142               0.114018              4.4   \n",
       "108     41.0  ...           0.003102               0.000000              0.5   \n",
       "109      NaN  ...           0.486810               0.763049              NaN   \n",
       "110    289.0  ...           0.245121               0.284211              2.4   \n",
       "\n",
       "     protectNorm_TET  protectNorm_PRP (Hib)  protectNorm_PT  protectNorm_PRN  \\\n",
       "106              3.0               2.600000          0.3125           0.3125   \n",
       "107              5.2              10.666667          0.3125           1.1250   \n",
       "108              0.5               1.800000          0.3125           0.3125   \n",
       "109             13.5              46.800000          3.3750           7.8750   \n",
       "110             24.5                    NaN          1.7500           2.5000   \n",
       "\n",
       "     protectNorm_FHA  geommean_protectNorm  VR_group_v2  \n",
       "106           1.3750              1.140388          NVR  \n",
       "107           0.3750              1.783418          NVR  \n",
       "108           0.1875              0.449420          LVR  \n",
       "109              NaN             11.383505          HVR  \n",
       "110           1.8750              3.440894          HVR  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titer_data = pd.read_csv('../../data/vaccine_response/vaccine_response_y1.tsv', sep='\\t', index_col=0)\n",
    "titer_data.index = [int(i.split('Baby')[-1]) for i in titer_data.index]\n",
    "titer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54eec5d-efc0-401d-bc27-efc2ce77a949",
   "metadata": {},
   "source": [
    "Use BabyN to connect compound abundance to titer values since there is only one 2 month sample and one set of 1 year titer values per baby."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a7b806f-edf7-481a-8870-2e5ccd59a03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PT</th>\n",
       "      <th>Dip</th>\n",
       "      <th>FHA</th>\n",
       "      <th>PRN</th>\n",
       "      <th>TET</th>\n",
       "      <th>PRP (Hib)</th>\n",
       "      <th>PCV ST1</th>\n",
       "      <th>PCV ST3</th>\n",
       "      <th>PCV ST4</th>\n",
       "      <th>PCV ST5</th>\n",
       "      <th>...</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>protectNorm_Dip</th>\n",
       "      <th>protectNorm_TET</th>\n",
       "      <th>protectNorm_PRP (Hib)</th>\n",
       "      <th>protectNorm_PT</th>\n",
       "      <th>protectNorm_PRN</th>\n",
       "      <th>protectNorm_FHA</th>\n",
       "      <th>geommean_protectNorm</th>\n",
       "      <th>VR_group_v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.39</td>\n",
       "      <td>141.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061955</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>1.375</td>\n",
       "      <td>1.140388</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958142</td>\n",
       "      <td>0.114018</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.375</td>\n",
       "      <td>1.783418</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.44942</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.44942</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P110_V9_05172019</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245121</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>2.4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.875</td>\n",
       "      <td>3.440894</td>\n",
       "      <td>HVR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
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      ],
      "text/plain": [
       "                     PT   Dip   FHA   PRN   TET PRP (Hib) PCV ST1 PCV ST3  \\\n",
       "P106_V9_04022019    2.5  0.21  11.0   2.5   0.3      0.39   141.0    35.0   \n",
       "P107_A1_04152019    2.5  0.44   3.0   9.0  0.52       1.6  2430.0   415.0   \n",
       "P108_V9_04022019    2.5  0.05   1.5   2.5  0.05      0.27    21.0     3.0   \n",
       "P108_V12_05212020   2.5  0.05   1.5   2.5  0.05      0.27    21.0     3.0   \n",
       "P110_V9_05172019   14.0  0.24  15.0  20.0  2.45       NaN   301.0    63.0   \n",
       "\n",
       "                  PCV ST4 PCV ST5  ... median_mmNorm_PCV  \\\n",
       "P106_V9_04022019     56.0   139.0  ...          0.061955   \n",
       "P107_A1_04152019    194.0   332.0  ...          0.958142   \n",
       "P108_V9_04022019     24.0    41.0  ...          0.003102   \n",
       "P108_V12_05212020    24.0    41.0  ...          0.003102   \n",
       "P110_V9_05172019    400.0   289.0  ...          0.245121   \n",
       "\n",
       "                  median_mmNorm_DTAPHib protectNorm_Dip protectNorm_TET  \\\n",
       "P106_V9_04022019               0.052874             2.1             3.0   \n",
       "P107_A1_04152019               0.114018             4.4             5.2   \n",
       "P108_V9_04022019                    0.0             0.5             0.5   \n",
       "P108_V12_05212020                   0.0             0.5             0.5   \n",
       "P110_V9_05172019               0.284211             2.4            24.5   \n",
       "\n",
       "                  protectNorm_PRP (Hib) protectNorm_PT protectNorm_PRN  \\\n",
       "P106_V9_04022019                    2.6         0.3125          0.3125   \n",
       "P107_A1_04152019              10.666667         0.3125           1.125   \n",
       "P108_V9_04022019                    1.8         0.3125          0.3125   \n",
       "P108_V12_05212020                   1.8         0.3125          0.3125   \n",
       "P110_V9_05172019                    NaN           1.75             2.5   \n",
       "\n",
       "                  protectNorm_FHA geommean_protectNorm VR_group_v2  \n",
       "P106_V9_04022019            1.375             1.140388         NVR  \n",
       "P107_A1_04152019            0.375             1.783418         NVR  \n",
       "P108_V9_04022019           0.1875              0.44942         LVR  \n",
       "P108_V12_05212020          0.1875              0.44942         LVR  \n",
       "P110_V9_05172019            1.875             3.440894         HVR  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_sample_titer_data = pd.DataFrame({sample: titer_data.loc[i] for sample, i in meta_base['BabyN'].iteritems() if i in titer_data.index}).transpose()\n",
    "per_sample_titer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4cf895-d716-479f-94bd-66fb124d9230",
   "metadata": {},
   "source": [
    "Merge sample metadata and titer data, filter out samples without one year titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20f8a3a2-43fd-4ba8-9b87-9f5da5d6dd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitDate</th>\n",
       "      <th>PT</th>\n",
       "      <th>Dip</th>\n",
       "      <th>FHA</th>\n",
       "      <th>PRN</th>\n",
       "      <th>TET</th>\n",
       "      <th>PRP (Hib)</th>\n",
       "      <th>PCV ST1</th>\n",
       "      <th>...</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>protectNorm_Dip</th>\n",
       "      <th>protectNorm_TET</th>\n",
       "      <th>protectNorm_PRP (Hib)</th>\n",
       "      <th>protectNorm_PT</th>\n",
       "      <th>protectNorm_PRN</th>\n",
       "      <th>protectNorm_FHA</th>\n",
       "      <th>geommean_protectNorm</th>\n",
       "      <th>VR_group_v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>106</td>\n",
       "      <td>V9</td>\n",
       "      <td>04022019</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.39</td>\n",
       "      <td>141.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061955</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>1.375</td>\n",
       "      <td>1.140388</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>107</td>\n",
       "      <td>A1</td>\n",
       "      <td>04152019</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958142</td>\n",
       "      <td>0.114018</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.375</td>\n",
       "      <td>1.783418</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>108</td>\n",
       "      <td>V9</td>\n",
       "      <td>04022019</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.44942</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>108</td>\n",
       "      <td>V12</td>\n",
       "      <td>05212020</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.44942</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P110_V9_05172019</th>\n",
       "      <td>110</td>\n",
       "      <td>V9</td>\n",
       "      <td>05172019</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245121</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>2.4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.875</td>\n",
       "      <td>3.440894</td>\n",
       "      <td>HVR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BabyN VisitCode VisitDate    PT   Dip   FHA   PRN   TET  \\\n",
       "P106_V9_04022019     106        V9  04022019   2.5  0.21  11.0   2.5   0.3   \n",
       "P107_A1_04152019     107        A1  04152019   2.5  0.44   3.0   9.0  0.52   \n",
       "P108_V9_04022019     108        V9  04022019   2.5  0.05   1.5   2.5  0.05   \n",
       "P108_V12_05212020    108       V12  05212020   2.5  0.05   1.5   2.5  0.05   \n",
       "P110_V9_05172019     110        V9  05172019  14.0  0.24  15.0  20.0  2.45   \n",
       "\n",
       "                  PRP (Hib) PCV ST1  ... median_mmNorm_PCV  \\\n",
       "P106_V9_04022019       0.39   141.0  ...          0.061955   \n",
       "P107_A1_04152019        1.6  2430.0  ...          0.958142   \n",
       "P108_V9_04022019       0.27    21.0  ...          0.003102   \n",
       "P108_V12_05212020      0.27    21.0  ...          0.003102   \n",
       "P110_V9_05172019        NaN   301.0  ...          0.245121   \n",
       "\n",
       "                  median_mmNorm_DTAPHib protectNorm_Dip protectNorm_TET  \\\n",
       "P106_V9_04022019               0.052874             2.1             3.0   \n",
       "P107_A1_04152019               0.114018             4.4             5.2   \n",
       "P108_V9_04022019                    0.0             0.5             0.5   \n",
       "P108_V12_05212020                   0.0             0.5             0.5   \n",
       "P110_V9_05172019               0.284211             2.4            24.5   \n",
       "\n",
       "                  protectNorm_PRP (Hib) protectNorm_PT protectNorm_PRN  \\\n",
       "P106_V9_04022019                    2.6         0.3125          0.3125   \n",
       "P107_A1_04152019              10.666667         0.3125           1.125   \n",
       "P108_V9_04022019                    1.8         0.3125          0.3125   \n",
       "P108_V12_05212020                   1.8         0.3125          0.3125   \n",
       "P110_V9_05172019                    NaN           1.75             2.5   \n",
       "\n",
       "                  protectNorm_FHA geommean_protectNorm VR_group_v2  \n",
       "P106_V9_04022019            1.375             1.140388         NVR  \n",
       "P107_A1_04152019            0.375             1.783418         NVR  \n",
       "P108_V9_04022019           0.1875              0.44942         LVR  \n",
       "P108_V12_05212020          0.1875              0.44942         LVR  \n",
       "P110_V9_05172019            1.875             3.440894         HVR  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta = pd.concat([meta_base, per_sample_titer_data], axis=1)\n",
    "meta = meta.loc[~pd.isna(meta['VR_group'])]\n",
    "meta.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf45137b-050f-4ab9-89b1-bbe6296846b9",
   "metadata": {},
   "source": [
    "Find shared samples between the metadata and metabolomics data. Filter metadata to match the metabolomics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d22833fb-4d09-4de6-a803-5662cdf76539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(141, 59)\n"
     ]
    }
   ],
   "source": [
    "in_both = list(set(meta.index) & set(metab_data.index))\n",
    "meta_matched = meta.loc[in_both]\n",
    "print(meta_matched.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c6f28aa-5692-4347-a4d5-072a5d3e2785",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V9     52\n",
       "V12    44\n",
       "V5     36\n",
       "V10     4\n",
       "A1      2\n",
       "A3      1\n",
       "S3      1\n",
       "A2      1\n",
       "Name: VisitCode, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_matched['VisitCode'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a439b0-b9da-4b27-afcd-caca8dff4d4e",
   "metadata": {},
   "source": [
    "Plurality of samples from 1 year. Many from 2 months and 2 years as well. We will focus on two months (V5) and 1 year (V9)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc99daf9-3980-4c54-a718-e84635f9fa20",
   "metadata": {},
   "source": [
    "## Do correlation with un-normalized data\n",
    "\n",
    "First take the \"raw\" data (not further normalized from what Tom gave me) and correlate each feature with the mei."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4751f63-b098-4ef0-8b56-eeb683a12052",
   "metadata": {},
   "source": [
    "### 2 months (V5) correlations\n",
    "\n",
    "First focus on the 2 month time points.\n",
    "\n",
    "We are using spearman for all of these correlations and multiple testing correction with Benjamini-Hochberg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65cbac06-e901-4ba4-b97b-bdc4ac6ebbae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 12 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.648906</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.002067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>0.413824</td>\n",
       "      <td>0.012110</td>\n",
       "      <td>0.296791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.401544</td>\n",
       "      <td>0.015208</td>\n",
       "      <td>0.296791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.397169</td>\n",
       "      <td>0.016462</td>\n",
       "      <td>0.296791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.392841</td>\n",
       "      <td>0.017787</td>\n",
       "      <td>0.296791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deoxycytidine 5-triphosphate</th>\n",
       "      <td>0.391248</td>\n",
       "      <td>0.018296</td>\n",
       "      <td>0.296791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>-0.389961</td>\n",
       "      <td>0.018717</td>\n",
       "      <td>0.296791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-3-Dihydroxyisovalerate</th>\n",
       "      <td>-0.381388</td>\n",
       "      <td>0.021728</td>\n",
       "      <td>0.301481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>-0.355773</td>\n",
       "      <td>0.033210</td>\n",
       "      <td>0.362375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.351351</td>\n",
       "      <td>0.035621</td>\n",
       "      <td>0.362375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Citrulline</th>\n",
       "      <td>-0.350837</td>\n",
       "      <td>0.035911</td>\n",
       "      <td>0.362375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.339768</td>\n",
       "      <td>0.042619</td>\n",
       "      <td>0.394225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kynurenic acid</th>\n",
       "      <td>-0.321493</td>\n",
       "      <td>0.055878</td>\n",
       "      <td>0.477111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>0.311712</td>\n",
       "      <td>0.064216</td>\n",
       "      <td>0.509142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "Phenylpyruvic acid           -0.648906  0.000019  0.002067\n",
       "Thymine                       0.413824  0.012110  0.296791\n",
       "Pyruvic acid                 -0.401544  0.015208  0.296791\n",
       "Inosine                      -0.397169  0.016462  0.296791\n",
       "Adenosine                    -0.392841  0.017787  0.296791\n",
       "Deoxycytidine 5-triphosphate  0.391248  0.018296  0.296791\n",
       "5-HIAA                       -0.389961  0.018717  0.296791\n",
       "2-3-Dihydroxyisovalerate     -0.381388  0.021728  0.301481\n",
       "2-Deoxyuridine               -0.355773  0.033210  0.362375\n",
       "xanthurenic acid             -0.351351  0.035621  0.362375\n",
       "L-Citrulline                 -0.350837  0.035911  0.362375\n",
       "L-Aspartic Acid              -0.339768  0.042619  0.394225\n",
       "kynurenic acid               -0.321493  0.055878  0.477111\n",
       "myo-Inositol                  0.311712  0.064216  0.509142"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_v5 = meta_matched.query(\"VisitCode == 'V5'\")\n",
    "metab_data_v5 = metab_data.loc[meta_v5.index]\n",
    "# metab_data_v5 = metab_data_v5[(metab_data_v5 > 0).sum(axis=0) > metab_data_v5.shape[0]*.2]\n",
    "v5_correlations = metab_data_v5.apply(spearmanr, b=meta_v5['median_mmNorm']).transpose()\n",
    "v5_correlations.columns = ['rho', 'p_value']\n",
    "v5_correlations['p_adj'] = p_adjust(v5_correlations['p_value'])\n",
    "v5_correlations = v5_correlations.sort_values('p_value')\n",
    "print(len(v5_correlations), len(v5_correlations.query('p_value < .05')), len(v5_correlations.query('p_adj < .05')))\n",
    "v5_correlations.head(14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ffee2da-8c7f-4883-8f9b-fe0d608c4464",
   "metadata": {},
   "source": [
    "13 compounds with raw p-values less than .05 and 1 compound with adjusted p-value less than .05.\n",
    "\n",
    "Now save correlations to file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f90f9f6-3031-4cd6-9af2-7efc68509e21",
   "metadata": {},
   "source": [
    "#### Plot phenylpyruvic acid result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fcd2781-197c-40f7-8132-351b27c49eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm', hue='VisitCode',\n",
    "                   data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V5'\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bac219f-578b-4bbe-81fc-97e1968941d5",
   "metadata": {},
   "source": [
    "Plot log phenylpyruvic acid abundance vs median normalized titer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3f4cc45-0c44-4332-9f31-16ab566a33cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(np.log10(metab_data.loc[meta_matched.query(\"VisitCode == 'V5'\").index, 'Phenylpyruvic acid']), meta_matched.query(\"VisitCode == 'V5'\")['median_mmNorm'])\n",
    "_ = plt.xlabel('Log Phenylpyruvic acid abundance')\n",
    "_ = plt.ylabel('median_mmNorm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "559d6dee-82ea-4fae-8b0b-218985848525",
   "metadata": {},
   "source": [
    "Plot ranks against each other since this is what spearman is actually using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb5d7cd1-8825-4414-b59e-4197fedd639f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ =sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm', hue='VisitCode',\n",
    "                   data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V5'\").rank())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1065224c-79e6-4d85-8d58-952e564594ae",
   "metadata": {},
   "source": [
    "### 1 year (V9) correlations\n",
    "\n",
    "Same correlations but with year 1 un-normalized data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d45b2557-fc9d-42a9-ac98-babe8ba4d779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 62 45\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.515677</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.506926</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Isoleucine</th>\n",
       "      <td>0.496435</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthine</th>\n",
       "      <td>0.496339</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.491516</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.487674</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>0.484045</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine</th>\n",
       "      <td>0.483212</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.468208</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.005731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.461430</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>0.006385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Methionine</th>\n",
       "      <td>0.443961</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.008416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthosine</th>\n",
       "      <td>0.443363</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.008416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>0.442732</td>\n",
       "      <td>0.001014</td>\n",
       "      <td>0.008416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riboflavin</th>\n",
       "      <td>0.441171</td>\n",
       "      <td>0.001062</td>\n",
       "      <td>0.008416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.438719</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.008439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>0.434228</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>0.009008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Kynurenine</th>\n",
       "      <td>0.429788</td>\n",
       "      <td>0.001474</td>\n",
       "      <td>0.009579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lactic acid</th>\n",
       "      <td>0.426202</td>\n",
       "      <td>0.001630</td>\n",
       "      <td>0.009579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <td>0.424409</td>\n",
       "      <td>0.001714</td>\n",
       "      <td>0.009579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Histidine</th>\n",
       "      <td>0.424162</td>\n",
       "      <td>0.001726</td>\n",
       "      <td>0.009579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-pantothenic acid</th>\n",
       "      <td>0.422275</td>\n",
       "      <td>0.001819</td>\n",
       "      <td>0.009614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Citrulline</th>\n",
       "      <td>0.414548</td>\n",
       "      <td>0.002247</td>\n",
       "      <td>0.010944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-asparagine</th>\n",
       "      <td>0.414216</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.010944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>0.407249</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.012309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>0.406694</td>\n",
       "      <td>0.002772</td>\n",
       "      <td>0.012309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <td>0.405021</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>0.012329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mevalonic acid</th>\n",
       "      <td>0.403705</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.012329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxycytidine 5-diphosphate</th>\n",
       "      <td>0.399991</td>\n",
       "      <td>0.003303</td>\n",
       "      <td>0.013096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Tyrosine</th>\n",
       "      <td>0.393289</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.015013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurine</th>\n",
       "      <td>0.384581</td>\n",
       "      <td>0.004878</td>\n",
       "      <td>0.018048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.378050</td>\n",
       "      <td>0.005723</td>\n",
       "      <td>0.020016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kynurenic acid</th>\n",
       "      <td>0.377708</td>\n",
       "      <td>0.005770</td>\n",
       "      <td>0.020016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatine</th>\n",
       "      <td>0.375061</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.020688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allantoin</th>\n",
       "      <td>0.371185</td>\n",
       "      <td>0.006746</td>\n",
       "      <td>0.022025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uridine</th>\n",
       "      <td>0.365286</td>\n",
       "      <td>0.007750</td>\n",
       "      <td>0.024577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uracil</th>\n",
       "      <td>0.361195</td>\n",
       "      <td>0.008519</td>\n",
       "      <td>0.026268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>0.355638</td>\n",
       "      <td>0.009670</td>\n",
       "      <td>0.029011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Pyridoxic acid</th>\n",
       "      <td>0.351881</td>\n",
       "      <td>0.010523</td>\n",
       "      <td>0.030737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanillic acid</th>\n",
       "      <td>0.341039</td>\n",
       "      <td>0.013355</td>\n",
       "      <td>0.038011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.339331</td>\n",
       "      <td>0.013856</td>\n",
       "      <td>0.038451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>0.337624</td>\n",
       "      <td>0.014373</td>\n",
       "      <td>0.038544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-phenyllactic acid</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.014584</td>\n",
       "      <td>0.038544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hypoxanthine</th>\n",
       "      <td>0.327037</td>\n",
       "      <td>0.017960</td>\n",
       "      <td>0.045790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Hydroxybenzoic acid</th>\n",
       "      <td>0.326525</td>\n",
       "      <td>0.018151</td>\n",
       "      <td>0.045790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL-2-Aminoadipic acid</th>\n",
       "      <td>0.323629</td>\n",
       "      <td>0.019265</td>\n",
       "      <td>0.047521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUDCA</th>\n",
       "      <td>0.318371</td>\n",
       "      <td>0.021436</td>\n",
       "      <td>0.051727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    rho   p_value     p_adj\n",
       "5-HIAA                         0.515677  0.000091  0.003955\n",
       "serotonin                      0.506926  0.000126  0.003955\n",
       "L-Isoleucine                   0.496435  0.000182  0.003955\n",
       "Xanthine                       0.496339  0.000182  0.003955\n",
       "L-Serine                       0.491516  0.000215  0.003955\n",
       "Guanosine                     -0.487674  0.000245  0.003955\n",
       "L-Proline                      0.484045  0.000277  0.003955\n",
       "Creatinine                     0.483212  0.000285  0.003955\n",
       "L-Phenylalanine                0.468208  0.000465  0.005731\n",
       "L-Leucine                      0.461430  0.000575  0.006385\n",
       "L-Methionine                   0.443961  0.000978  0.008416\n",
       "Xanthosine                     0.443363  0.000995  0.008416\n",
       "Orotic acid                    0.442732  0.001014  0.008416\n",
       "Riboflavin                     0.441171  0.001062  0.008416\n",
       "Adenosine                     -0.438719  0.001140  0.008439\n",
       "hydroxyphenyllactate           0.434228  0.001298  0.009008\n",
       "L-Kynurenine                   0.429788  0.001474  0.009579\n",
       "Lactic acid                    0.426202  0.001630  0.009579\n",
       "Phenaceturic acid              0.424409  0.001714  0.009579\n",
       "L-Histidine                    0.424162  0.001726  0.009579\n",
       "D-pantothenic acid             0.422275  0.001819  0.009614\n",
       "L-Citrulline                   0.414548  0.002247  0.010944\n",
       "L-asparagine                   0.414216  0.002268  0.010944\n",
       "L-Threonine                    0.407249  0.002732  0.012309\n",
       "xanthurenic acid               0.406694  0.002772  0.012309\n",
       "N-Acetyl D-galactosamine       0.405021  0.002897  0.012329\n",
       "Mevalonic acid                 0.403705  0.002999  0.012329\n",
       "2-Deoxycytidine 5-diphosphate  0.399991  0.003303  0.013096\n",
       "L-Tyrosine                     0.393289  0.003922  0.015013\n",
       "Taurine                        0.384581  0.004878  0.018048\n",
       "Inosine                       -0.378050  0.005723  0.020016\n",
       "kynurenic acid                 0.377708  0.005770  0.020016\n",
       "Creatine                       0.375061  0.006150  0.020688\n",
       "Allantoin                      0.371185  0.006746  0.022025\n",
       "Uridine                        0.365286  0.007750  0.024577\n",
       "Uracil                         0.361195  0.008519  0.026268\n",
       "N-Acetylneuraminic acid        0.355638  0.009670  0.029011\n",
       "4-Pyridoxic acid               0.351881  0.010523  0.030737\n",
       "Vanillic acid                  0.341039  0.013355  0.038011\n",
       "L-tryptophan-D5               -0.339331  0.013856  0.038451\n",
       "L-Glutamine                    0.337624  0.014373  0.038544\n",
       "3-phenyllactic acid            0.336941  0.014584  0.038544\n",
       "Hypoxanthine                   0.327037  0.017960  0.045790\n",
       "4-Hydroxybenzoic acid          0.326525  0.018151  0.045790\n",
       "DL-2-Aminoadipic acid          0.323629  0.019265  0.047521\n",
       "TUDCA                          0.318371  0.021436  0.051727"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_v9 = meta_matched.query(\"VisitCode == 'V9'\")\n",
    "metab_data_v9 = metab_data.loc[meta_v9.index]\n",
    "# metab_data_v9 = metab_data_v9[(metab_data_v9 > 0).sum(axis=0) > metab_data_v9.shape[0]*.2]\n",
    "v9_correlations = metab_data_v9.apply(spearmanr, b=meta_v9['median_mmNorm']).transpose()\n",
    "v9_correlations.columns = ['rho', 'p_value']\n",
    "v9_correlations = v9_correlations.loc[~pd.isna(v9_correlations['p_value'])]\n",
    "v9_correlations['p_adj'] = p_adjust(v9_correlations['p_value'])\n",
    "v9_correlations = v9_correlations.sort_values('p_value')\n",
    "print(len(v9_correlations), len(v9_correlations.query('p_value < .05')), len(v9_correlations.query('p_adj < .05')))\n",
    "v9_correlations.head(46)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03d5953-d901-4d7c-83a8-ce7cb9894b88",
   "metadata": {},
   "source": [
    "45 compounds have significant adjusted p-values. 62 significant raw p-values. So about half of things are significant. Is this an artifact or does it mean that base metabolism is associated with titer response?\n",
    "\n",
    "Tryptophan D5 is in this list. It is a control compound so we probably shouldn't trust this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0348866c-9d90-42d7-a36c-2cff5ee4ee48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.339331</td>\n",
       "      <td>0.013856</td>\n",
       "      <td>0.038451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      rho   p_value     p_adj\n",
       "L-tryptophan-D5 -0.339331  0.013856  0.038451"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_correlations.loc[[i for i in v9_correlations.index if 'D5' in i]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98af5b1-0b99-4f9d-8f65-6436b95dd424",
   "metadata": {},
   "source": [
    "#### 5-HIAA\n",
    "\n",
    "At 1 year correlation between 5-HIAA and median titer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6bab5203-523b-414c-803d-c7ee65fd4844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cc46ko3tVPfbYY9qxY0eJNpmZmbr++us1Y8YMSTpuLtj111+vxMREZWZm6g9/+IMOHDhQfCw7O1s33nij9u/fr+bNm+uqq64qPnb77bfLbrfrm2++0T/+8Y/igBYIBPTCCy9o5syZpb6f22+/XVarVV999ZWefPLJEnerMjMzdcMNNygQCKhVq1Y699xzK+yf6sCcLAAAAKASbDabxo4dqzfffFMzZsyQ3W4vc7nx0jgcDr3wwgu69tprNX36dM2ePVtt2rSRxWJRdna2PB6P3G63nnrqqXI3NS7So0cPLVu2TI8++qg+/PBDXX755cV32yTpH//4h1JTU/XOO+/ovffe0/vvv68WLVooNTVV+/fvLw5niYmJeuihhzR27NgS52/durVefPFF/eEPf9CcOXM0fPhwderUSaFQSJs2bVI4HFZaWppefvnlEnOvunTpoieeeEIPPvig3nrrLU2ePFlt2rRRdna2cnNzNXr0aM2aNeu493PKKafoscce08MPP6x3331XH330kTp27KiCggJt27ZNpmmqadOmevvttyPqn+rAnSwAAACgko4NMWeeeWapCzuUZ+DAgZo8ebLOP/98paamauvWrdq+fbuaN2+u3/3ud5o+fXqZm/X+0hNPPKGhQ4fKZrNpy5Yt2rp1a4njhmHo/vvv16effqorrrhCXbp0UV5entauXau8vDz17t1bt99+u2bMmHFcwCoybNgwTZs2TVdffbXS09O1ZcsW7d69W927d9c999yjL774ongZ92Odd955mjx5ss455xy5XC5lZGQoNTVVf/nLXzRhwoQy39OFF16oL774QpdccolSU1O1ceNG7d69Wx07dtR1112nL7/8Uu3bt4+of6qDYf5y8Cbibv369fJ4PEpISCh1v4BYC4VCWrFihSSpT58+JXYdR3TR1/FBP8cH/Rw/9HV80M/xUd39HA6HixeT6Nq1qyyWunnPwTRNeTweST+v+FefVfbrXtXr87r5XQUAAAAA1YSQBQAAAABRRMgCAAAAgCgiZAEAAABAFBGyAAAAACCKCFkAAAAAEEWELAAAAACIIlt1FxANW7Zs0SuvvKLly5frwIEDatGihc4++2zdeOONSkhIiPg8Xq9X/fr1UygUKrPNJ598op49e0ajbAAAAAB1UK0PWatWrdJVV10lj8ejXr16qWfPnvrxxx/12muvae7cufrggw+UlJQU0bk2bNigUCiktLQ0nXLKKaW2SU5OjmL1AAAAAOqaWh2ygsGg7r77bnk8Hj3++OMaP368pKN3pO666y7NmTNHzz77rB566KGIzrd27VpJ0rnnnqu77747ZnUDAAAAqLtq9ZysadOmKTs7W4MHDy4OWJLkcrn0xBNPKCEhQR9//LEOHz4c0fmKQhbDAQEAAACcqFodsubMmSNJGj169HHHUlJSNHDgQAUCAS1YsCCi8xGyAAAAAFRVrR4umJmZKUnq2rVrqcc7deqkuXPnasOGDTrnnHPKPZff71dWVpaSk5P13Xff6b///a82bdok0zTVq1cvXXfddRo6dGjU38OxTNMsd9GNWDn2Navj9esT+jo+6Of4oJ/jh76OD/o5Pqq7n8PhsEzTlGEYMk1TpmnGvYZ4OPZ91dX3WBlFfVB0vV1Rn1S1z2p1yNq7d68kqXnz5qUeb9asWYl25dmwYYMCgYByc3P1wAMPqE+fPho4cKA2bdqkxYsXa/Hixbrnnnt0ww03RO8N/EJhYaFWrFgRs/NHYvXq1dX6+vUJfR0f9HN80M/xQ1/HB/0cH9XZzy6XSx6PRxZLrR7YFZHCwsLqLqHahcNhhcNheb1erVq1KuavV6tDlsfjkXT0h6Q0RY8XtSvPunXrJB0NZq+++mqJIYOfffaZJkyYoGeffVZ9+/bVqaeeWtXSAQAAANRRtTpkWa1WhcNhGYZRbrtIbvddfPHFOv3002W1Wo+7M3bBBRdo7dq1mjRpkiZOnBizkOV2u8sc+hhLoVCo+JOknj17ymq1xr2G+oK+jg/6OT7o5/ihr+ODfo6P6u7ncDisjRs3yjAMJSQk1Nk7WaZpFt/BcrvdFV4vn4ibb75Zc+fO1fnnn6+nn366wvZz5szRLbfcoubNm2vOnDmyWq1as2aN3nzzTf3www/Kz89XWlqazjzzTF199dVq0qRJ1GoNh8OyWCxyu93q3LlzhV/3jIyMKt0BrNUhKzExUbm5uWV2gNfrlXT0G6siFotFaWlpZR4/44wzNGnSpJje1jYMo9p/oVut1mqvob6gr+ODfo4P+jl+6Ov4oJ/jozr62TCM4sBx7L/rsli9z0suuURz587VrFmz9MgjjyghIaHc9p9++qkkafz48bLZbPriiy/04IMPKhgMqmXLlho6dKh27typN998U19++aXeeOMNdevWLSq1Hvs1t1qtFYasqvZXrY7uRXOu9u3bV+rxorlYRe2qokWLFpIY0woAAABI0umnn64WLVrI4/Fo5syZ5bY9ePCg5s+fL4vFoosuukjZ2dmaMGGCgsGgfv/73+ubb77Rv/71L3355Zd65plntG/fPt1xxx3y+XxxejfRVatDVtHQuk2bNpV6vOjxSIbgvfrqq7rjjjv03XfflXp8z549kn4OWwAAAEA0Hcrz6r/fZOgvry/WvS9+q7+8vlgff5OpQ3ne6i6tVFarVRdeeKEk6fPPPy+37RdffKFAIKDhw4erZcuW+uSTTxQIBNS/f3/98Y9/lM328wC7c845R+PHj9e2bds0efLkWL6FmKnVIWv48OGSpBkzZhx37NChQ1qyZInsdntES69v2bJFM2bM0GeffVbq8aLHR4wYceIFAwAAAL/gC4T08uQV+v1jM/Xe/zZoReY+ZWw7pBWZ+zTpf+v1+8dm6uXJK+QP1LytBcaPHy+LxaIlS5YU35QoTdFQwYsvvljS0ZW9JenMM88sdWje4MGDJUnz58+PdslxUatD1ujRo5WWlqaFCxfq/fffL37c6/VqwoQJ8ng8Gj9+vJo2bVp8LBAIKCsrS1lZWQoEAsWPX3755TIMQ19++aW+/PLLEq8zceJEffHFF0pOTtaVV14Z+zcGAACAesEXCOmRN7/TjO+3KRgqfbG2YMjUjO+36eE3v5OvhgWttLQ0DRs2TOFw+Lhr6CKrVq1SZmamWrRoUXyTpGiPtKSkpFKfUzRXLysrKwZVx16tDlkul0tPPfWUXC6XHn30UY0bN0533HGHRo8erdmzZ6tHjx669957SzwnJydHY8aM0ZgxY5STk1P8eN++fXX33XfLNE3dd999Ov/883XHHXfo7LPP1uOPP66EhAS98sorUV3lBAAAAPXbm5+v1pqsAxG1XZN1QG9+XvP2cCu6O1XWkMFPPvlEknTRRRcVh6cOHTpIkpYsWVLqc5YvXy5JOnAgsr6paWp1yJKkgQMHavLkyTrrrLO0a9cuzZs3Tw0aNNAtt9yiSZMmlZmOS3PDDTfo3Xff1fDhw7Vnzx7NmTNHXq9XF198sb766iv1798/hu8EAAAA9cmhI17NXra9Us+ZvWx7jZujNXLkSKWmpiorK+u4lbh9Pp+mT58uq9Wq8ePHFz8+btw4GYahqVOn6uOPPy7xnHnz5umDDz6QJPn9/ti/gRio1Uu4F+nSpYtefPHFiNqmp6crIyOjzOODBw8uHgMKAAAAxMrMpWUPESxLMGRq1pLtuvjMLjGqqvJsNpvGjRun119/XV988YV69uxZfGzGjBnKy8vTyJEjSywg161bN9133336+9//rr/85S/697//rY4dO2r37t1au3atfve732nSpEklFsSoTWr9nSwAAACgNop0mOAvrc7aH+VKqu6iiy6SYRiaNm1aiXUPpkyZIkm69NJLj3vOtddeq7feekuDBg1STk6OFi9eLLfbrZdeekk33XSTJKlRo0bxeQNRVjujIQAAAFDLFfqCcX1eLLVu3VqDBw/W4sWL9e233+qMM87Qzp07tWTJErVs2VKnn356qc877bTTdNpppx33+Pfffy9JatWqVUzrjhXuZAEAAADVwO08sfsdJ/q8WCtaAOOLL76QdHQLJNM0ddFFF8liKRk7Dh48qO+//16ZmZmlnqto79pjhx7WJoQsAAAAoBqc3PHEVq3u2bFpxY2qwZlnnqkmTZpo/vz58ng8pS54UWTDhg266qqr9OSTTx53zOv1Fg8z/M1vfhPzumOBkAUAAABUg18NaCub9fiNeMtjsxoaPbBNjCqqGrvdrrFjx8rr9eqtt95SVlaWRowYoebNmx/Xtn///kpJSdF3332nb775pvhxn8+nP/7xj9q3b59Gjhypvn37xvMtRA0hCwAAAKgGKQ1dOuPUygWmM05to5QGrhhVVHVFQwbfeOMNSdIll1xSajuHw6Gnn35aknTbbbfpiiuu0G233aaRI0dqxowZ6t69u5544on4FB0DhCwAAACgmlw/tmfEwwZP7thEN4yt2XOU2rVrpwEDBigQCKhVq1alLmpRZPjw4Xr33Xc1ePBgrV+/XosWLVJqaqruu+8+vf/++2rcuHEcK4+umjlrDgAAAKgHnHarHrl+sN78fLVmL9te6r5ZNquhM05toxvG9pTDbq2GKitn0qRJEbcdNGiQBg0aFMNqqgchCwAAAKhGTrtVt13UR1f8uptmLdmu1Vn7VegLyu20qWfHpho9sGYPEcTxCFkAAABADZDSwKWLz+yii8/sUt2loIqYkwUAAAAAUUTIAgAAAIAoImQBAAAAQBQRsgAAAAAgighZAAAAABBFhCwAAADUG4ZhFP87HA5XYyWIp2O/1sd+D8QKIQsAAAD1hmEYstmO7mLk9XqruRrES9HX2mazEbIAAACAaGvQoIEk6dChQzJNs5qrQayZpqlDhw5J+vlrH2tsRgwAAIB6pVGjRjp06JDy8/O1Y8cOpaSkyOVyyWKpO/cfTNMsHiIXDofjcvempgmHw/J6vcVfa+no1z4eCFkAAACoV9xut1q1aqWdO3cqPz+/+AK8rikKWXUpPFZFq1at5Ha74/JahCwAAADUOw0bNpTdbtfhw4eVl5enYDBY3SVFlWmaxfOQ3G53vbyTJR2dg9WgQQM1atQobgFLImQBAACgnnK73XK73WrRooVM06xT87NCoZBWrVolSercubOsVms1VxR/hmFUW7gkZAEAAKDeq84L8lg4NjBaLBaGDMYZvQ0AAAAAUUTIAgAAAIAoImQBAAAAQBQRsgAAAAAgighZAAAAABBFhCwAAAAAiCJCFgAAAABEESELAAAAAKKIzYgBAFHlD4QUCpuyWgyFw6YMw5DTYa3usgAAiBtCFgAgKnz+kIKhsKYv3qLZy7J1pMCnJLdDw/ul69zT2stht8rl4M8OAKDu468dAKDKAkFTG7Nz9de3v5fPHyp+PM8T0EezMvTJnI26/8r+6t0llaAFAKjzmJMFAKiS5ORkHTzi1SNvflciYB0rGArryf8s046c/DhXBwBA/BGyAABV0jC5id6fsUH+YLjcdqGwqfe+Xi+PNxCnygAAqB6ELADACbPZbEpMTNTiVbsjav9jxl4FQ2aMqwIAoHoRsgAAJ8xut+vQkUIFQ+XfxSpimtK+Q54YVwUAQPUiZAEATphpmrJZK/enpLLtAQCobfhLBwA4YT6fT42SnGqa7IqofZLbrpZNE2NcFQAA1YuQBQA4YaZp6uDBgxozpH1E7c8c2EZhkzlZAIC6jZAFAKiSgwf26dzTOqhjeqNy26U3S9Jlo7uyTxYAoM4jZAEAqsTn88luNfTkLcM0rE+aLBajxHGLIQ04qYX+ecfpBCwAQL3AXzsAQFS4nTbdflEf3TKut+Yuz9bBI141THRoVP/WctitSnDZq7tEAADigpAFAIiaoiB13ukdFQyFZbUYMgyjgmcBAFC3ELIAADHBUu0AgPqKv4AAAAAAEEWELAAAAACIIkIWAAAAAEQRIQsAAAAAooiQBQAAAABRRMgCAAAAgCgiZAEAAABAFBGyAAAAACCKCFkAAAAAEEWELAAAAACIIkIWAAAAAEQRIQsAAAAAooiQBQAAAABRRMgCAAAAgCgiZAEAAABAFBGyAAAAACCKCFkAAAAAEEWELAAAAACIIkIWAAAAAEQRIQsAAAAAooiQBQAAAABRRMgCAAAAgCgiZAEAAABAFBGyAAAAACCKCFkAAAAAEEWELAAAAACIIkIWAAAAAEQRIQsAAAAAooiQBQAAAABRRMgCAAAAgCgiZAEAAABAFBGyAAAAACCK6kTI2rJli+69916NHDlSvXr10q9+9Ss999xz8ng8VT73008/ra5du+qll16KQqUAAAAA6rpaH7JWrVqlcePGaerUqWratKlGjBghj8ej1157TZdeeqny8/NP+NyLFi3SO++8E8VqAQAAANR1tTpkBYNB3X333fJ4PHr88cc1efJkvfjii/rmm280atQoZWRk6Nlnnz2hcx88eFB/+tOfZJpmlKsGAAAAUJfV6pA1bdo0ZWdna/DgwRo/fnzx4y6XS0888YQSEhL08ccf6/Dhw5U+94MPPqhDhw6pX79+0SwZAAAAQB1Xq0PWnDlzJEmjR48+7lhKSooGDhyoQCCgBQsWVOq877//vubOnatbb71VJ598clRqBQAAAFA/2Kq7gKrIzMyUJHXt2rXU4506ddLcuXO1YcMGnXPOORGdc+PGjXr66afVr18/3XjjjXrqqaeiVm9FTNNUKBSK2+sVOfY1q+P16xP6Oj7o5/ign+OHvo4P+jk+6Of4oJ+rpqpThmp1yNq7d68kqXnz5qUeb9asWYl2FfH5fLr77rtlt9v1j3/8Q1arNTqFRqiwsFArVqyI62v+0urVq6v19esT+jo+6Of4oJ/jh76OD/o5Pujn+KCf469Wh6yiJdpdLlepx4sej3Qp97///e/KzMzU008/rfT09OgUCQAAAKBeqdUhy2q1KhwOyzCMcttFcrtv3rx5eu+99zRmzBiNHTs2ShVWjtvtLnPoYyyFQqHiTzh69uwZ9zt49Ql9HR/0c3zQz/FDX8cH/Rwf9HN80M9Vk5GRocLCwhN+fq0OWYmJicrNzS2zA7xer6Sj4aU8+/fv1wMPPKCWLVvqr3/9a9TrjJRhGNX+A2C1Wqu9hvqCvo4P+jk+6Of4oa/jg36OD/o5PujnyqvoJk5FanXIatasmXJzc7Vv3z61bt36uONFc7GK5maV5dVXX9XBgwfVvXt3PfrooyWOrV27VpI0c+ZMbdu2TR07dtTNN98cpXcAAAAAoK6p1SGra9euyszM1KZNm0rdz2rTpk3F7cpTNGdr/fr1Wr9+faltMjMzlZmZqQEDBhCyAAAAAJSpVu+TNXz4cEnSjBkzjjt26NAhLVmyRHa7XUOHDi33PE899ZQyMjJK/e/KK6+UJN12223KyMjQpEmTov9GAAAAANQZtTpkjR49WmlpaVq4cKHef//94se9Xq8mTJggj8ej8ePHq2nTpsXHAoGAsrKylJWVpUAgUB1lA6il/IGQ/IGQ1m05oDk/ZOu71bvl8Qbk8fK7BAAA/KxWDxd0uVx66qmndMMNN+jRRx/VlClTlJ6erp9++kl79+5Vjx49dO+995Z4Tk5OjsaMGSNJmj17Nku1A4iI1x/UN0u365M5G3XgsLf4cZvVomG903TjuF5KcFplsdTqz64AAEAU1PqrgYEDB2ry5Mk666yztGvXLs2bN08NGjTQLbfcokmTJikpKam6SwRQy3n9QU2ctk6vf7a6RMCSpGAorHk/7tDdz82XxxeqpgoBAEBNUqvvZBXp0qWLXnzxxYjapqenKyMjI+JzT5gwQRMmTDjR0gDUAVt2HtHUhVvKbbP7QIFem7JSt4zvrQSXPU6VAQCAmqjW38kCgFjyeAP6ZO7GiNouWrVbEex9DgAA6jhCFgCUw+Ww6Yf1ORG1DYbCWrFxX4wrAgAANR0hCwDKEQqbCocjvz1VyEqDAADUe4QsACiHxZAaJEQ+x6p544QYVgMAAGoDQhYAlCMYCuvMAW0iapvSwKmubRvHuCIAAFDTEbIAoBxOh03jRnSSy2GtsO24kZ3FuhcAAICQBQAVSHDZ9cj1g8sNWmcPaadfD24rp73iMAYAAOq2OrFPFgDEksNuVefWyXrjwTP16bxN+mbJduUXBmQxpL5dm2n8qM7q1DpZLge/UgEAACELACLisFvlsFt1xVnddPWYHgqZpmwWi7z+IJsPAwCAEghZAFAJRXerigYFErAAAMAvMScLAAAAAKKIkAUAAAAAUUTIAgAAAIAoImQBAAAAQBQRsgAAAAAgighZAAAAABBFhCwAAAAAiCJCFgAAAABEESELqASPN6Bw2FQobCoUCsvrC1Z3SQAAAKhhbNVdAFAbeH1B7T9cqE/mbNTClbvk84eU4LJp5CnpunBkZzVIdMjl4McJAAAAhCygQl5fULN/2K7XP1st0/z5cY83qGmLtmrG99t032/7q1+3ZgQtAAAAMFwQKE84bGrDtkN67dOSAetYwZCpv0/6QTkHPPEtDgAAADUSIQsohy8Q0vsz1lfYLhQ29cHMDfJ4A3GoCgAAADVZTMc27dq1S+vWrVN+fn6FbceOHRvLUoAT4vEGtGHroYjaLlmzR5ZLjRhXBAAAgJouJiHL7/frgQce0PTp0yNqbxgGIQs10v7cwojbhsKmjhT45XIyLwsAAKA+i8nV4Kuvvqpp06ZJkux2u1JSUmSzceGJ2sfpsFaqvd3OCFwAAID6LibJZ9q0aTIMQ7feeqtuvPFG2e32WLwMEHNpTZPUKMmhw/n+CtumN0tSgpPvdQAAgPouJh+779mzRy1bttRtt91GwEKtZsrUWYPaRtR27OkdZbUyJwsAAKC+i0nIatSokRITE2NxaiCunHabLjqjizqlJ5fbrm/XVI04pbVsVoYLAgAA1HcxuSIcNmyYtm7dqgMHDsTi9EBcuRw2PXHLUP1qYBs5bCV/ZNxOm8YO76gJVw+o9PwtAAAA1E0xmZN1++23a9asWfrjH/+oF154QUlJSbF4GSBu3E6brjvvZF13fk/9uGGvDuf71KSRS326pCpsSk4HC7sAAADgqJhcGbZq1Uqvv/66rrzySo0YMUL9+vVT48aNZRilz1cxDENPPPFELEoBosbtOjq/cGjvtGquBAAAADVZTELW3r179eCDD8o0TeXn5+vbb78ttZ1hGDJNk5AFAAAAoM6ISch65plntH37dhmGoT59+qhVq1ZyOByxeCkAAAAAqFFiErIWLVokm82mt956S4MGDYrFSwAAAABAjRST1QWPHDmi9u3bE7AAAAAA1DsxCVnp6enyer2xODUAAAAA1GgxCVnnnXeeduzYoYULF8bi9AAAAABQY8UkZF177bXq06eP7rzzTr399tvatGmT8vPzFQ6Hy/wPAAAAAOqCmCx8cfXVV8vv9ys/P1///Oc/9c9//rPc9oZhaN26dbEoBQAAAADiKiYha/ny5cX/Nk0zFi8BAAAAADVSTELWxIkTY3FaAAAAAKjxYhKyDMNQ9+7dlZSUFIvTAwAAAECNFZOFL/785z9r+PDhOnToUCxODwAAAAA1VkzuZO3evVvt2rVTSkpKLE4PnLBCX1DhsClTphx2qxw2a3WXBAAAgDomJiGrcePGysvLk2maMgwjFi8BVIrXH9T+XK8+m7dJO/fly2ox1KdLqsYMaS+LxZDbGZMfBQAAANRDMRkuePPNN2v37t164okn5PV6Y/ESQMS8/qD+PukH3fz0bM1csk1rNx/Qqk37NXH6ev324f/pm6Xb5fUHq7tMAAAA1BEx+/i+X79+eu+99zR58mR1795dqampcrlcpbY1DENPP/10rEpBPeYLhPTEO0v1U+a+Uo8HQ6be+Hy1XA6rTu/bSk4Hd7QAAABQNTG5onz44YdlGIZM05TX69VPP/1UaruiNoQsxMrmnYfLDFjHenfaOg3vlx6HigAAAFDXxSRkjR07lrlYqHYeb0Cfzt0UUdsjBX4tW7dHQ3ql8b0LAACAKolJyHrqqadicVqgUiyGoc07cyNuv37rIQ04qYXsrDgIAACAKojJwhdATWBKlborxQ0sAAAARENMZ/mHw2FNmzZN33zzjbZs2aKCggIlJiaqXbt2Ou200zR27FjZ7fZYloB6zDRNdWmTopyDnoja9+zYlLtYAAAAqLKYhaycnBzdcsstWrdunUzTLHEsMzNTs2bN0ocffqiXXnpJrVq1ilUZqMcSXHZdOLKTFqzYWWHblAZO9emSGoeqAAAAUNfFJGT5fD5df/31yszMlMvl0plnnqmTTz5ZiYmJOnLkiNasWaM5c+Zo3bp1uvXWW/Xxxx/L4XDEohTUc+nNkjSkV0stXrW73HY3juulX3wWAAAAAJyQmISsDz/8UJmZmWrfvr3efPNNpacfvzR2dna2rr/+emVkZGjKlCm67LLLYlEK6jmnw6a7LztFbudKzfkh+7gg5XbadOv43jqlazM5HQwVBAAAQNXFJGT973//k2EYeu6550oNWJLUunVrPf/88xo7dqy++uorQhZixumw6sYLeumq3/TQ1AWblZ2TJ6vFolO6NdPpfVvJlORiE2IAAABESUyuLLOystSuXTt169at3HbdunVT+/bttXnz5liUARRzO21yO226dHRXBYJhyZCcdqtsVhbYBAAAQHTF5ArT5/MpMTExorYJCQnyeCJb/Q2oKofdqkS3XYkuOwELAAAAMRGTq8yWLVsqKytLBQUF5bYrKChQVlaWmjVrFosyAAAAACDuYhKyBg8eLK/Xq7///e/ltnv66afl8/k0ePDgWJQBAAAAAHEXk5B1zTXXyG636+OPP9b111+vBQsW6ODBgwoEAjp48KAWLFig6667TpMnT5bdbtc111wTizIAAAAAIO5isvBFu3bt9Le//U0PPvigFi5cqIULFx7XxjRN2Ww2PfbYY2rfvn0sygAAAACAuIvZzP/zzjtP77//voYMGSKbzSbTNIv/s1qtGjp0qN577z2df/75sSoBAAAAAOIuppsD9e7dW2+//bYKCwuVnZ2t/Px8JSYmqk2bNnK73bF8aQAAAACoFnHZgdXtdqtLly7xeCkAAAAAqFZsFAQAAAAAUVTlO1lnnHFGlYswDEPffPNNlc8DAAAAANWtyiFr586dVS7CMIwqnwMAAAAAaoIqh6wnn3yy0s9ZuXKl/vvf/0o6upQ7S7gDAAAAqCuqHLIuuOCCiNsGAgG98MIL+uSTTyQdvYN19dVX66677qpqGQAAAABQI8RldUFJWr16tR544AFlZWXJNE21a9dOTz75pPr27RuvEgAAAAAg5mIesgKBgF5++WW9/fbbCgaDslgsuvrqq3XnnXfK6XTG+uUBAAAAIK5iGrLWrl2r+++/X5s2beLuFQAAAIB6ISYhKxgM6uWXX9Zbb73F3SsAAAAA9UrUQ9a6det0//33a+PGjcV3r5544gn169cv2i8FAAAAADVO1EJWKBTSK6+8ojfeeKP47tVVV12lu+66i7tXAAAAAOqNqISsDRs26P7771dGRoZM01Tbtm315JNPcvcKAAAAQL1T5ZD18ssv67XXXiu+e3XFFVfo7rvvlsvlUjgcjvg8FovlhGvYsmWLXnnlFS1fvlwHDhxQixYtdPbZZ+vGG29UQkJCpc713Xff6e2339bKlSvl8/nUsmVLnXnmmbr++uuVnJx8wjUCAAAAqB+iErIMw5BhGDJNU++//77ef//9Sp3DMAytW7fuhF5/1apVuuqqq+TxeNSrVy/17NlTP/74o1577TXNnTtXH3zwgZKSkiI615QpU/Tggw9Kkvr06aOmTZtq3bp1euutt/Tll1/qww8/VHp6+gnVCQAAAKB+OPHbR8cwTbPK/52IYDCou+++Wx6PR48//rgmT56sF198Ud98841GjRqljIwMPfvssxGdKzs7Ww8//LDsdrveeecd/fe//9Urr7yimTNn6sILL9TevXv16KOPnlCdAAAAAOqPKt/JmjhxYjTqOCHTpk1Tdna2Bg8erPHjxxc/7nK59MQTT2jUqFH6+OOP9Yc//EGNGjUq91xTp05VIBDQpZdeqiFDhhQ/brfbdc8992jKlClauHCh/H6/HA5HzN4TAAAAgNqtyiFrwIAB0ajjhMyZM0eSNHr06OOOpaSkaODAgZo7d64WLFigc845p9xz3XTTTTrnnHNKncMVCoUkHZ03VpW5YwAAAADqvphsRhwvmZmZkqSuXbuWerxTp06aO3euNmzYUGHIslgsatOmzXGPHzp0SI888ogk6fzzz5fNFrsuM02zONDF07GvWR2vX5/Q1/FBP8cH/Rw/9HV80M/xQT/HB/1cNSc6nalITEOW3+/X5s2blZ+fX2Ghp556aqXPv3fvXklS8+bNSz3erFmzEu0q44033tDChQu1YsUK+f1+jR07Vg899FClz1MZhYWFWrFiRUxfoyKrV6+u1tevT+jr+KCf44N+jh/6Oj7o5/ign+ODfo6/mIWs5557ThMnTpTX662w7YmuLujxeCQdnYNVmqLHi9pVxvTp07V+/XpJktVq1ZEjR7Rjxw517Nix0ucCAAAAUH/EJGS9++67ev311yNuf6K346xWq8LhsAzDiPr5X3vtNaWkpGjbtm169dVX9b///U8//vijPv/8c7Vs2fKE6q2I2+0uc+hjLIVCoeJPOHr27Cmr1Rr3GuoL+jo+6Of4oJ/jh76OD/o5Pujn+KCfqyYjI0OFhYUn/PyYhKyPP/5YhmFo3LhxuvXWW9WsWbOYzGVKTExUbm5umR1QdBfN7XZX+twtWrSQJHXp0kXPP/+88vLytHDhQv373//WhAkTTrzochiGUe0/AFartdprqC/o6/ign+ODfo4f+jo+6Of4oJ/jg36uvIpu4lQkJkvlZWdnq0mTJnrssceUlpYWs8UiiuZc7du3r9TjRXOxitpVxXnnnSdJJ7xpMgAAAID6ISYhKykpSampqTFf7rxoaN2mTZtKPV70eCRD8CZOnKh77rlHa9asKfV40d5YwWDwREoFAAAAUE/EJAWdeuqp2rJliwoKCmJx+mLDhw+XJM2YMeO4Y4cOHdKSJUtkt9s1dOjQCs+1YsUKffXVV/r0009LPT5//nxJR8e0AgAAAEBZYhKybrnlFoVCIf31r3+t8hrz5Rk9erTS0tK0cOFCvf/++8WPe71eTZgwQR6PR+PHj1fTpk2LjwUCAWVlZSkrK0uBQKD48csvv1yS9NFHHxVvciwdXTRj0qRJ+uyzz+R0OnXllVfG7P0AAAAAqP1iMlmqW7dueuGFF3TbbbdpxYoVGjp0qBo3blzuBLLbbrut0q/jcrn01FNP6YYbbtCjjz6qKVOmKD09XT/99JP27t2rHj166N577y3xnJycHI0ZM0aSNHv2bKWnp0uS+vfvr7vuukvPPfecbr75Zp188slq0aKFMjMztX37drndbj3zzDOlblgMAAAAAEViErLy8/P1xhtvKBwOKzs7Wx999FGFzzmRkCVJAwcO1OTJk/Xyyy9r6dKl2rRpk9LT0zV+/Hhde+21SkpKivhcN910k3r16qV33nlHK1euVEZGhlJTUzV+/Hhdf/31ateu3QnVCAAAAKD+iEnIeu6557RixQpJUmpqqtLS0mS322PxUpKOLrP+4osvRtQ2PT1dGRkZZR4fMmSIhgwZEq3SAAAAANQzMQlZs2fPlmEYeuKJJ3TBBRfE4iUAAAAAoEaKycIXBw8eVNu2bQlYAAAAAOqdmISsZs2asas0AAAAgHopJiHrrLPO0ubNm7V27dpYnB4AAAAAaqyYhKybb75Zbdq00c0336yvv/465psSAwAAAEBNEZOFLx599FG1bt1aCxcu1F133SXDMNSgQQO53e5S2xuGoblz58aiFAAAAACIq5iErC+//LL436ZpyjRNHT58WIcPHy61fXmbFAMAAABAbRKTkPXkk0/G4rQAAAAAUOPFJGSxdDsAAACA+iomC18AAAAAQH0VkztZRXbt2qV169YpPz+/wrZjx46NZSkAAAAAEBcxCVl+v18PPPCApk+fHlF7wzAIWQAAAADqhJiErFdffVXTpk2TJNntdqWkpMhmi+lNMwAAAACoEWKSfKZNmybDMHTrrbfqxhtvlN1uj8XLAAAAAECNE5OQtWfPHrVs2VK33XZbLE4PAAAAADVWTEJWo0aNlJiYGItTAwCAWiwYDCsUDstqtchmZZFjAHVTTELWsGHDNG3aNB04cEBNmjSJxUsAAIBaIhw25QuElJvn06yl25TvCSi5gVNnDWqrBKddbhfztgHULTH5rXb77bdr1qxZ+uMf/6gXXnhBSUlJsXgZAABQw4VCYeUVBvTku0u1bsvBEsc+nJmhU7o1032/7S+30yaLxaimKgEgumISslq1aqXXX39dV155pUaMGKF+/fqpcePGMozSf3kahqEnnngiFqUAAIBqVOgL6u7n52vfocJSjy/fsFd/fHmB/nnH6XI7uaMFoG6IyW+zvXv36sEHH5RpmsrPz9e3335bajvDMGSaJiELAIA6yOsLatL/1pcZsIps35OnqQuyNHZ4Jzns1jhVBwCxE5OQ9cwzz2j79u0yDEN9+vRRq1at5HA4YvFSAACghjIMae7yHRG1nb54q8YO7xTjigAgPmISshYtWiSbzaa33npLgwYNisVLAACAGm7nvgIV+oIRtT1w2Ku8Ar+aJLtjXBUAxF5M1k49cuSI2rdvT8ACAKAeC4XDlWtvmjGqBADiKyYhKz09XV6vNxanBgAAtUTzxomKdMFAp92qRknO2BYEAHESk5B13nnnaceOHVq4cGEsTg8AAGoBm9VQv27NI2p7Wt9WCoe5kwWgbohJyLr22mvVp08f3XnnnXr77be1adMm5efnKxwOl/kfAACoWxJcdl01prts1vIvN9xOmy7/VVeWcAdQZ8Tkt9nVV18tv9+v/Px8/fOf/9Q///nPctsbhqF169bFohQAAFCNWjZN0sPXDdLf3lkinz903PEkt12P3TiEoYIA6pSYhKzly5cX/9tkEisAAPWW02FV93YpmvjwWZq5ZJvmLd+h/MKAGiU5deaANhp5SroshsH+WADqlJiErIkTJ8bitAAAoBZyOo5ebvxmaHv9amBbGYYh0zTlsFsrHEoIALVRTELWgAEDqvT8BQsW6MCBAxo7dmx0CgIAANXObrPKbuOOFYC6r0Z+fPSvf/1LDzzwQHWXAQAAAACVViNDFgAAAADUVoQsAAAAAIgiQhYAAAAARBEhCwAAAACiiJAFAAAAAFFEyAIAAACAKCJkAQAAAEAUxWQzYgCINn8wpEAgLEmy2yxy2NnQFFVjmqYKfUGZpmQYUoLLXt0lAQDqCEIWgBqt0BdUKBTW9MVbtGHbIcmUurRN0W+GtpfNapHbya8xVE4wFFY4bGrdloP6Zuk25RcG1DDRqV8PbquO6cmyWw1ZLAz0AACcOK5OANRYPn9Qn87dqMmzNyoUNosfX7Y+Rx/NzNAFIzrp0tFd5HTwqwyRCQRD2neoUA+98Z1yDnpKHJu7PFutmzfQozcMVnIDp2xWghYA4MTwFwRAjVToC+qTORv10azMEgGrSChs6pM5G/XhzAwV+oLVUCFqozxPQPe++O1xAatIdk6e7nnhW76nAABVQsgCUCOFQmFNnr2xwnafzc9SIBiKQ0Wo7Qq9Af1n2jrleQLltjt4xKv/zsqUl6AFADhBNTJkmebxn1oDqD/8wZCmL95S6h2sXwqHTU1dsEW+AEELFTAMLVixM6Km3yzdJsNixLggAEBdVSMnMjz00EPKy8ur7jIAVJNAIHx0kYsIZWw/qEAwLCcrDqIce/YXKBAMR9S2wBvU4TyfmjVOiHFVAIC6KKYh69ChQ1q1apXy8vIUDJY/7GLs2LHF/+7evXssywJQG1TmhrZZ2SegPqrsd4jJ9xQA4ATFLGQ9++yz+ve//61QqOIhPIZhlAhZAOo3u82iLm1TtGx9TkTtO7dJlt3GXSyUr2XTRNltlojuZiW4bEpu4IpDVQCAuigmIevTTz/VG2+8IelogGrcuLGcTmcsXgpAHeSwW/Wboe310cyMCudlWQzpvNM6MlQQFTJNU0N7pWnejzsqbHtG/9YyI5gTCABAaWISsj7++GMZhqHzzjtPDzzwgJKTk2PxMgDqMJvVogtGdNInc8pfYfC80zrIQcBCBBJcdl31mx5atj5HBYVlrzCY3MCpS3/VTS42ugYAnKCYrC6YmZmphg0b6rHHHiNgATghbqdNl47uqnEjO6m0Rd4shnT+6R3027N7yM3FMCLUKMmhf9x+mpomlz4UsGWTRP3zjtPldhLcAQAnLmZXJunp6XI4HLE6PYB6wOmwHg1aIzrpq4WblbHtkExJXVon69zTOsrpsMrp4GIYkbPbrGrRJEGv33+mVm7cp2+WbleeJ6BGDRz69aB26t6usawWQ1ZrjdzhBABQS8QkZHXs2FHbt2+XaZoyDPYZAXDi3E6b3E6bxp/RRcFgWKZM2a2EK5y4okVSTunWXD3aN5EpydDRxS74mwUAiIaYfFR38cUX6/Dhw/rggw9icXoA9ZDTblWi264kt4OAhaiwWIz//56yK9FtJ2ABAKImJneyLrroIv3www968skntW3bNo0cOVLNmjUrd/hg69atY1EKAAAAAMRVTELWsGHDJEnBYFCTJk3SpEmTym1vGIbWrVsXi1IAAAAAIK5iErL2799f/G/TrHifkUjaAAAAAEBtEJOQNXv27FicFgAAAABqvJiErFatWsXitAAAAABQ49WIjUC8Xm91lwAAAAAAURGzzYhN09S3336rzMxMeb1ehcPhEsdDoZAKCwuVk5OjJUuWaMmSJbEqBQAAAADiJiYhy+fz6brrrtMPP/xQYVs2LAYAAABQl8RkuOAHH3ygZcuWyTRNpaen66STTpJpmmrVqpX69Omjli1bFq8o2LdvX7377ruxKAMAAAAA4i4mIWvGjBkyDEP33nuvZs2apQ8++EBOp1M9evTQhx9+qDlz5ujtt99Ww4YNlZmZqfT09FiUAQAAAABxF5OQtWXLFjVo0EDXXHONJMnhcKhr164lhg8OHTpUf/nLX1RQUKD//Oc/sSgDAAAAAOIuJiGroKBA6enpslqtxY916tRJhw4d0t69e4sfO/vss9WoUSMtXrw4FmUAAAAAQNzFJGQlJiYqEAiUeKx169aSpKysrOLHrFar0tPTtWvXrliUAQAAAABxF5OQ1aZNG2VnZysvL6/EY6ZpKiMjo0Tb/Pz845Z3BwAAAIDaKiYha8iQIfJ6vZowYYIOHz4sSTr55JMlSVOmTJHP55MkLV++XNu2bVPLli1jUQYAAAAAxF1MQtZvf/tbNWzYULNmzdLw4cPl9/vVtm1bnXrqqdq0aZPGjRunO+64Q9dff70Mw9DgwYNjUQYAAAAAxF1MQlZqaqreeOMNpaeny+l0yuFwSJLuvfdeOZ1OZWVladasWfJ4PEpJSdEtt9wSizIAAAAA1BL+QEgFhQHlFwZU6AtWdzlVYovVifv06aMZM2Zow4YNxY/17t1bU6ZM0cSJE7Vjxw516NBBv//979W0adNYlQEAAACgBvP6ggqFTU1fvEU/ZuxVMBRWq9QkjR3eSS2aJMjliFlkiZmYVmyxWNSjR48Sj3Xs2FF//etfY/myAAAAAGoBry+o2cu2660v1ygYMosf37D1kGYvy1bfLql64OoBcjtrV9CKyXBBAAAAACiPzx/SolW79Npnq0sErGP9lLlPj771vfyBUJyrq5oqR8I//vGPMgxD9913X/Gwvz/+8Y+VOodhGHr66aerWgoAAACAWsIwpHe+WlthuzWbD2jDtoPq1Sk1DlVFR5VD1pdffinDMHTTTTcVh6yix0yz9ERapKgNIQsAAACoX37K3KvD+f6I2n46d5M6pScrwWWPcVXRUeWQNXbsWBmGoQYNGhz3GAAAAAD8UjAU1votByNuv2XXEVkstSdfVDlkPfXUUxE9BgAAAACSZEiVuilT2+7f1K5lOsqwZcsWvfLKK1q+fLkOHDigFi1a6Oyzz9aNN96ohISESp1r3rx5eu+997RmzRrl5eUpOTlZ/fr103XXXafevXvH6B0AAAAA9YfValHvzk31yZyNEbXv2iZF4XD5U5Fqklq/uuCqVas0btw4TZ06VU2bNtWIESPk8Xj02muv6dJLL1V+fn7E53r22Wd14403auHChWrVqpVGjBihhg0baubMmbrsssv02WefxfCdAHWPxxso3lQwFApXdzkAAKAG6dG+iZomuyJqe+GozrVmPpYUhTtZI0aMqHIRhmFo7ty5lX5eMBjU3XffLY/Ho8cff1zjx4+XJHm9Xt11112aM2eOnn32WT300EMVnuuHH37Q66+/Lrfbrddff10DBw4sPvbRRx/p4Ycf1kMPPaSBAwcqLS2t0rUC9UU4bCoQCmvj9kOatmiL9h8ulNth07A+rTS8bytJhpwOa3WXCQAAqpsh3Tyut/72zhKVt17eoJNbqG2LhvGrKwqqHLL27NlT5SJOdJGMadOmKTs7W4MHDy4OWJLkcrn0xBNPaNSoUfr444/1hz/8QY0aNSr3XJ988okk6brrrisRsCTp0ksv1Zw5czR//nzNmDFD11xzzQnVC9R14XBYRzwB/eW1xdq6+0iJYz9l7tNbX6zRA1efqu7tGtfK3dsBAED0OGxW9erUVPdfeape/O9PKvAGSxw3DGnkKa11y4W9a90HtFW+ynnyySdLffyzzz7T0qVL1b59e40fP17du3dXw4YN5fV6tWnTJk2ZMkWrV6/Wr371K1111VUn9Npz5syRJI0ePfq4YykpKRo4cKDmzp2rBQsW6Jxzzin3XC6XS126dNGgQYNKPd6hQwfNnz9fOTk5J1QrUB/4AmH98cUF2n2goNTjhb6gHn3rez1922nqlJ5cq1YJAgAA0edy2nRKt2b6zyO/1uJVu/TD+hwFg2GlN0vSOad1kMthq3UBS4pCyLrggguOe2zRokVaunSpzj77bP3973+X3V5y/GT//v11ySWX6M9//rM+/fRTnX322Sf02pmZmZKkrl27lnq8U6dOmjt3rjZs2FBhyHrkkUfKPb5y5UpJUosWLSpfKFAPBIIhzfhua5kBq0gwZOrNL1br0RuGyO3kbhYAAPWd8/9Ht5zep5UGntRCpiS71SKHvfaFqyIxucJ57bXX5Ha79dhjjx0XsIoYhqG//OUv+vrrr/XOO++cUNDau3evJKl58+alHm/WrFmJdidqzpw5+vHHH2W320u9axYtpmkqFArF7PxlOfY1q+P165O63NemKX21aEtEbTdsPaTD+T45bLG5k1WX+7kmoZ/jh76OD/o5Pujn+Kit/ey0/7wuX3XWbZY3SSwCMQlZa9euVceOHZWUlFRuO5fLpQ4dOmjjxsiWbvwlj8dTfJ6yzn9suxORkZGhBx54QNLR+VqtWrU64XNVpLCwUCtWrIjZ+SOxevXqan39+qQu9bXValW37icp52DkP2ubsg/JmxfUgQMHYlhZ3ernmox+jh/6Oj7o5/ign+ODfo6/mCzh7nA4dPBgxTs4m6ap3bt3KzEx8YRex2o9eguxooUzTjSJrlq1SldddZVyc3M1cuRI3XHHHSd0HqBeqOQCNoZR9U+JAAAAaqKY3Mnq2rWrli5dqunTp2vMmDFltps4caL279+vUaNGndDrJCYmKjc3V4WFhaUe93q9kiS3213pc3/99de6//77VVhYqDPPPFPPPfecLJbYbivmdrvLnF8WS6FQqPgTjp49exaHV0RfXe7rYMhUerMk7dhb8d50hiF1a9dEyUkOtWnTJuq11OV+rkno5/ihr6vm2A9jy/twh36OD/o5PujnqsnIyCgzY0QiJiHrt7/9rZYsWaL7779fW7du1QUXXKCWLVsWH9+2bZs+/PBDTZo0SRaLRb///e9P6HWaNWum3Nxc7du3T61btz7ueNFcrKK5WZF65ZVX9NJLL8k0TV1++eX685//HJdvTMMwqv0HwGq1VnsN9UVd62tTYZ13eke9+snKCtv26pSqBKc9Lu+/rvVzTUU/xw99HZlw2JQvEFJegV/frd4trz+o1s0b6JTuzWWGTbkqWHiHfo4P+jk+6OfKO9EtporEJGSNHj1al112mT788EO99NJLeumll5SYmCi3262CgoLiVGiapu677z7179//hF6na9euyszM1KZNm9SvX7/jjm/atKm4XSTC4bAefPBBffbZZ7Jarbr//vt15ZVXnlBtQH1js1o06pR0/W/xFm3ZdaTMdk6HVTde0FMuJ7/sAcRGIBhSbp5Pz37wo9ZsLjnvM9Ft1wUjOmrs6R2LVzQDgGiL2fi3hx9+WH//+9/Vrl07maap/Px87du3Tx6PR6Zpqnv37nr77bd17bXXnvBrDB8+XJI0Y8aM444dOnRIS5Yskd1u19ChQyM635///Gd99tlnSkhI0L/+9S8CFuLKFwipoDAgjzegcDhc3eWcEIfdqidvHaZenZuWerxJI5eevnWYmjV2V/kTIgAojWmays3z6Q/PzjsuYElSQWFA7/1vg978Yo28/mApZwCAqovpRzjnnXeezjvvPGVnZ2vLli06cuSIkpOT1b59+6is0jd69GilpaVp4cKFev/993XFFVdIOjoXa8KECfJ4PLrsssvUtOnPF3yBQEDbt2+XJLVp06Z4ifnPP/9cU6ZMkc1m06uvvqrBgwdXuT4gEoW+oPILA/pqQZZ2H/DIYbNo0MktNeCkFjJN1aoN+AzDUKLLrr9cM1AHjnj11cLN2p/rldtp1Yh+rXVyxyYyDMluqz3vCUDt4vUF9fx/f1KeJ1Buuxnfb9PoAW3VtW1KnCoDUJ/E5T5569atS50zVVUul0tPPfWUbrjhBj366KOaMmWK0tPT9dNPP2nv3r3q0aOH7r333hLPycnJKV6MY/bs2UpPT1coFNLzzz8vSWrSpImmTJmiKVOmlPqaw4YN09ixY6P+XlA/ef1BPf/hj1q8eneJx+f/tFMNEx2694pT1L19Y7lOYEhL0XyEUCisAm9QiS6brFaLnHarLJbY3kVyOW1qlZqkq37TQ6GQKcOQ3E4bd69QrwWCIYVNyW6zyDQlnz+oBFfpe0nixBV4g1q1cX9EbafM3ag7L+3L1wFA1MU8ZGVlZWnevHnavHmz8vLy9OKLL8rj8WjGjBk655xzytysOFIDBw7U5MmT9fLLL2vp0qXatGmT0tPTNX78eF177bUV7tUlHV09ZPfuoxe5OTk5mjp1apltGzZsSMhClVksFgXD0mP/XlLmxcCRAr8eeet7/e3GIerevrFs1shH93p9Qa3ZfECTZ2dq3Zaft1M4qUMTXXRGZ53UockJBbfKisdrADVdMBRWIBjWjO+3avqirdpzsEBWi0V9u6TqwlGd1Ck9pVbdsa7plq3Pibjtjxl75bTT9wCiL2ZXQIWFhXrkkUc0depUmaYp0zSLP8XesWOHHnjgAb3yyiv697//XeUlnLt06aIXX3wxorbp6enKyMgo8ViPHj2OewyIpcaNG2tt1v4KP20Nh029OmWlnr9reMQhy+sP6qNZGZoyd9Nxx9ZuPqC1mw9o/KjOuuTMLhWurgWgaoKhsPYdKtSfXl6gQ3m+Eo8vW5+jZetzdEb/1rr5wl4swhAlPn8o4raBYFhGjO/sA6ifYrLwRTgc1i233KIvv/xShmGoR48eSkn5ecyzz+eTzWbTjh07dMUVV0S0cTFQlzRMbqJP52VF1HbH3nxl51S895QkhcJh/ZSxt9SAdaxP5mzUyk37au0CG0BtEQyFdf8rC0sErF+a/UO2Js/eKK+PRRiioXXzikewFGnROEGBQOShDAAiFZOQNWXKFH333Xdq27Zt8YIS7du3Lz7es2dP/e9//1P79u21f/9+vfvuu7EoA6ixkhLdytgW+YcLKzfui6idPxDW5NkbI2o7+ZuN8gUIWUCsBENhzf0hWwePeCts++WCzcxZjJKeHZsqOckZUdsxQ9vHfI4qgPopJiHr888/l2EYev7559W5c+dS27Ru3VovvPCCJGnu3LmxKAOosQxJZiXamxG2LvQFtTE7N6K2GdsP8ck5EEOBYFjTF2+NqG2hL6jv1+6uuCEqZJrSRWeUfu1xrJQGTv1qYFtWOwUQEzEJWZmZmWrTpo26detWbrsuXbqobdu2ys7OjkUZQI2V7/GqQ1qjiNv3aN8konZ5Bf5K1ZHnqVx7AJGzWS3KOeiJuP2OnDyFw5X5+AWlcTqs+tWgtho7vGOZbVIaOPXUbcNkt8Vsu1AA9VxMZtn6fD4lJCRE1DYxMVGmyR8V1C9Hcg9q7PCOWr+14iGDzRsnqFN6ckTndbsq9yPNssVA7IRNU067VYUR3jF2OWwMXYsSl8OmK37dTWf0b61P5m7S8vU58gdCat4kUWOGtNOZp7aRzWap1KqtAFAZMQlZLVq00NatW+X3++VwOMps5/F4lJWVpebNm8eiDKDGOnjwgPqf1FOdWydXOLzvuvNPVqRTNRolOtS6eQNl5+RV2LZtiwZKSiBkAbESDps6pXtzzV62PaL2Q3unxbii+sXlsKldWiPdemEv2WwWWSyGAsGwrBYLd7AAxFxMfssMGTJEXq9Xr7/+erntXnzxRfl8Pg0ePDgWZQA1VigUktUi/e2mIerRvnGpbRw2i+65vJ/6dE6NeM6A1WrRuBFlD5E51rgRnWQv5VPcQl9QPn9Q2/Yc0bbdR1ToC6rQy9wtoLLcTpvGj6p4bpAk9WjfWI0iXKwBleN22WW3WWW1WORy2AhYAOIiJneyrr32Wn3++ef617/+pfz8fJ177rny+4/O/fB6vcrMzNSkSZP01VdfyWaz6aqrropFGUCNl+Cy69EbhmjH3jx98W2W9hzwyG63aGCPFho9sK0MqVJ7WdmsFp3Wp5VWbtyveT/uKLPdyFPSNaR3mqzHhKxAMCSPN6hJ/1uveT/uKN5rxm6zaFjvNF05pocaJjnkYJI4ELGmjVy65Mwu+u83mWW2aZBg1z2Xn8KmuABQh8QkZLVu3Vr/+Mc/dM8992jixImaOHFi8bG+fftKkkzTlNVq1aOPPqoOHTrEogygVnA6rOqYnqybxvXS0TnvR+dxnOiKV06HTbdd1Fs92jfWZ/OztHt/QfGxtKaJGjeyk0b0Sy+x8WkoHNahPJ/uef5b5eaX3M8nEAxr7vIdWrp2j56+/TSlNU1kNS4gQi6nTePP6KwmyW59NDOjxHLuhiH16pSqOy7po5QGTuZjAUAdErPt5UePHq2PP/5YL730khYsWFB8J0uSrFarBgwYoNtvv139+vWLVQlArRLNRSicDptGD2irUf3baH9uofI8fjVIcKhpsltWiyHbL4bLBEOmHn7ju+MC1rEKvEE99Pp3evPBM6NWJ1AfuBw2ndG/tc7o31rrtx7U9j1H5LRbdUr35kpw2uVyWtkjCwDqmJiFLEnq1q2bXnnlFfn9fm3btk15eXlKSEhQ69atlZiYGMuXBuo9m80im6RWzZIqbLt5x2Ht2JtfYbuDR7xanrFXA3u04FN3oBIc/z8UsHfnVPXunFrN1QAAYi2mIcvv92vz5s3Kz88vXqY9Ly9P69atO67tqaeeGstSAJSh0BfUzKXbIm4/a8k29erYVIluViYEAAAoTcxC1nPPPaeJEyfK6/VW2NYwjFKDF1AbBIJFC0TUznlK4bBZqU2M8z2BGFYDAABQ+8UkZL377rsVLt9+LDYjRm3j8wdlGIZ+WJ+jdVsO/v8E9qbFw4ActWiVMIvFUKOksvez+6WGiZG3BQAAqI9iErI+/vhjGYahcePG6dZbb1WzZs1ks8V0ZCIQN15fUAtW7NS709bpyDF3gD6fn6WUBk7dOK6XTunarFJLr1cnt9Omswa108wlkW2Y+qtBbeWuJe8NAACgOsRkR77s7Gw1adJEjz32mNLS0ghYqDO8vqC+WbZdL368okTAKnIoz6en/rNMS9bukddfezbwbduiodq1bFhhu9Rkt/p0TmXRCwAAgHLEJGQlJSUpNTVVFgu7qqNuCYVNvf3lmgrbvfLJStWmGGK3GXrk+kFq3NBVZpsGCXY9dtMQAhYAAEAFYpKCTj31VG3ZskUFBQUVNwZqCX8wpOmLtygYqngOYaEvqG9/2qlQKByHyqrOYrGoUZJTL983Uued1kGJrp/vPrscVv16UFu9ct8oNUtxy2blw5O6JBw2VegLyh8IyeMNKBgKy+urPXdhAQCoiWIyju+WW27R3Llz9de//lVPP/00myyiTvAHQlqRuS/i9ss37NWQ3mlKrCWhxGa1qEGCQ787u7uu/E0PHTzslSlTTRq6FDbFPKw6yOcPasXGffp07iat23JQ0tGv86j+6Ro/qosaJDrkrEWLuAAAUFPE5KqpW7dueuGFF3TbbbdpxYoVGjp0qBo3blxu2LrttttiUQoQNYYMhcKRr4QZCoelWrhwZtGCHS2bsmF4XebzB/XCf3/SghW7Sjxe6Atq2qKt+mZZth65bpA6t0khaAEAUEkxCVn5+fl64403FA6HlZ2drY8++qjC5xCyUNNZLIZapSZp7eYDEbVv3byBbLbacRcL9YvPH9SUuZuOC1gl24T0yFvf6+0JowlZAABUUkxC1nPPPacVK1ZIklJTU5WWlia73R6LlwLixu206YIRHTVzybYK2xqGdO6wDlycomYyDE1dsLnCZj5/SJ/Pz9Klo7vK6eB7GQCASMUkZM2ePVuGYeiJJ57QBRdcEIuXAKpF02S3Tu3eXMvW55TbblT/1rVmnyzUP6s27lN+YSCitt8s3a4rzuoW44oAAKhbYjKW6eDBg2rbti0BC3WOy2HTH6/sr75dUstsM6RXS908rndcF4oIBMPyBULasPWgpszZqClzNipj+yH5/CEFg7VjhUPEz96Dnojb5ub7WLYfAIBKislVYLNmzWS1MrQEdZPLYdOE3w/U5h2H9em8jdq887AMw1DnNsm6cGRntW6WFNehVV5/UGs3H9Drn63W7v0lt01Ia5qomy/spW5tG3NnDcUSEyIfvu1gXiEAAJUWk6uus846S//+97+1du1anXTSSbF4CaBaOe1WdW/fWHe17CeLYciUZJqmElzxnXvo9Qf144a9emriMpmlrGS4a3+BHn7jOz14zQD16Zwqp4OgBenU7i1ksxoR7fk2uGdLef3BuH9vAwBQm8XkI8qbb75Zbdq00c0336yvv/6aTYlRZyW47HI5bXI7bdV2Efrchz+WGrCKhE3p2Q9+lMSQLxxlGNLgnmkRtR0/qjMBCwCASorJx9qPPvqoWrdurYULF+quu+6SYRhq0KCB3G53qe0Nw9DcuXNjUQpQZwVDYc1bvkNef6jCth5vUItW7dTwvumy1pLNkRE7CS67bh3fW1k7crVrf9kfgl1+Vle1aMJ+aQAAVFZMQtaXX35Z/G/TNGWapg4fPqzDhw+X2r68TYoBlM7nD2nJ2j0Rt/9u9R4NPKmlEt2ELEhup1XP3jlcb3+5Rt/+tFO+wM9hvVVqki4d3UWDeraUiyGmAABUWkz+ej755JOxOC2AX/AHK76LVSQQDEmqeA4O6geLxaJEt0XXj+2p68f21IZtB+Xzh9S8cYLSmibJajVk464nAAAnJCYhi6XbgdizWAy1bJKoVRv3R9S+RZNEhgriOEVbDfTt0qyaKwEAoO7giguopdxOm847vWPE7c8/vQNDvwAAAOKAkAXUYs1S3OrdueyNkYuc0q2ZUhq44lARAAAACFlALeZy2DTh6gHq1i6lzDY92jfWn6489YQ3I/Z4AyX+AwAAQPkYOwTUcm6XTY/fNFQ/Ze7VZ/OytGlHrgxJnVona9yITurdOVUOu7XS5/X5g9q5r0CfztukrB25kqTOrVN04chOat4kgaGHAAAAZeAqCagDHHarTu3eQj07Ni0OVP5ASC6HTRZL5bdI8PqDeub95fp+Tckl4nfszdfc5dka1idNd17ST05H5cMbAABAXUfIAuoIi8VQgste/P9PdPntQl9QL3284riAdayFK3bJZbfqhgt6Fa9OBwAAgKOYkwWghCMFfi1YsbPCdrN/yFahLxiHigAAAGoXQhaAYl5/UJ/P3xRRW9OUvlywWT5/5BsiAwAA1AeELADFQiFT23PyIm6/ffcRBUPhGFYEAABQ+xCyAJRgrcRCGVZr5RfVAAAAqOsIWQCKOewW9epU8ebGRXp3TpXzBJaHBwAAqMsIWQCK2W1WnT24XUQrEzrtVp1xahvZbPwaiYQvEJLHG5CXxUIAAKjzWHsZQAkWi6Grz+mht75YU267684/WQwWrFihL6jcPJ+mL96ig0e8SnDaNKp/G3VIbySbxZD1BJfaBwAANRchC0AJbqdNZw1qK4fNov9MW6cCb8k7Lw0S7Pr9uSdrWJ80uRz8CimLaZryeIN64j9LtWrj/hLHvv5+m1qlJumhaweqabK7eANpAABQN3CFBOA4LodNI/u31qhT2+i7Vbu0futByTB0cocmGnBSC5mmScCqgC8Q0p9eXqBte0pfrXHnvnzd/cK3eunekUpNdse5OgAAEEtcJQEoVVGIOr1vugb3TJMMyW61yFKJ1Qfrq0AwpBnfbS0zYBUpKAzorc9X6w+X9lWCyx6n6gAAQKwRsgCUy2Ix5HQwnK0yTFOaunBLRG2XrN2jcNiMcUUAACCemHENAFHmD4aVc9ATUdtQ2NTmXYdjXBEAAIgnQhYARJlpVu7OFHeyAACoWwhZABBlLodVjZIcEbdv3bxhDKsBAADxxpwsAIiycNjUJaO7aOP2XFmtFh04XKhVG/crVModq16dmyrByZw3AADqEkIWgJjx+YMKhkwZxtHVCuvLyoShsKnhfVurYYJTgWBYrZol6Q+XuPX199v02dxN8gVCkiSb1aLfn3uSXE5+FQMAUJfwlx1A1Hl9QeXm+/Tlgs3ae9Ajp8OqYb3SdEr35pJUZzffDQTDyivw61+frtTSdTkl5lq1bdFAl/2qm/528xA98sZ3CpvShGsGqFVqkgyjfoRPAADqC0IWgKgq9AX19MRlWr5hb4nHv/1ppxolOXT/laeqU+vkOreZcThsKjfPqzufm68jBf7jjm/bk6enJi7TTeN66bm7hiuloUuGWB4fAIC6iIUvUOt4vAGFw6ZCYVPBYFiBQEiFvqOP+QMhhcOmCn3B6i6zXvIFQvrL64uPC1hFDuf79ZfXF2v7njyFwuE4Vxdb/kBIT0/8odSAdaw3Plsll8Mml8NGwAIAoI6qWx8lo07z+oLanpOnKXM2aum6PXLYrXro2oFKdDv02bxNWrhip/zBsOw2i4b1TtP4M7qoWbKb+S5xEg6HtXTtHmVsO1Ruu2DI1KtTVuqpW4bJ6qw7n/McOOxVxvby37skhU3pi2+zdNmvuhGyAACoo7j6RK3g9Qf1wcwN+mxelqSjCwb85fcDtW1Pnl7/dJWOXbQtEAxr7vIdmvfjDt00rpdGndKaoBUHvkBYn8/Piqht1o7D2pdbqNbNG8S4qvgIhU3NXZ4dcfvvVu/WJaO7xrAiAABQnerOx8ios/zBkOb+kF0csCRpVP90BUOmXvtFwDqWaUr/mrJKG7Nz2ew1Dpx2qzZmV3wnp8j6LQdiWE18hcNhef2RD1H1+kOysNgFAAB1FiELNZ8pTZ69scRDY4a216fzNsqMIDt9ODOjeMlsxFAlM0MkX7vawmqxqFnjhIjbN2nkqnNz0gAAwM8IWajxsnYeHVpWpGWTRCUnObUic19Ez1+dtZ+QFQd+f0jtWjaMuH3nNikxrCa+LBZDZ/RvI5s1sqR59uB2dXYZewAAQMhCLbB7f0GJ/98wyaH9ud5K3QnJzfNGuarY8AdC8vqDCoXCR/9di1ZJtNssOv/0jhG1bduigdJSE2NcUXwZhjTilNYVtktp4NTpfVvJZuXXLwAAdRWrAaDGcztLfuLv84fkquSqbE57zf5W9wdC8gdDmrZwi75Ztl2Hjvjkdtk0pFdLjRvRWY0SHTV+8Q6r1aJhvVvp8/lZ2rr7SJntLIZ0/diedS5kJLjsuumCntqfW1jmXdZGSQ49eeswWevYewcAACXxlx41Xq9OqSWGYe3cl69GSU61aBLZHJjUFLeaNHLFqrwq8wdCytp5WL9/bJbe+3qD9hzwyBcIKTfPp+mLtur6J2Zp7vLsWnFXy2G36Mlbh6prGUMBXQ6rHrx6gLq2SalzIUuSnA6b/nzNQP3xd/3VuXVy8ePJDZy6dHRXvXb/mWqeklAn3zsAAPhZzf5oHNDRYVhDeqXp2592Sjq6RPs3y7ZrzJD2+vfUtRU+/zdD21d6UYZ4OlLg10OvL5bXX/q8MdOUXp2ySqkpCerbNVVWS829QDcMQ0luhx6/eai27zmiLxds1t5DHjntVg3u2VIj+x8dTudy1N1fPU6HVUN7tdSp3ZvLajUUDpuyWCwKh032xQIAoJ6ou1c6qDMSXHbdPK6XMrYdUs5BjyRp+qIteubO0/Vjxt5yF8A4uWMTnTO0vRy2mnlxW+gL6r/fZJQZsI713v/W6+QOTWrFBr5Oh1Wd26To5gt7yTQlU6ZcDlu9uYNjsVjkqgVfJwAAEBtcBaBWcLtseu6u4RraK01Wi6F9uYV66j/LdM/lp+iCEZ2U6Cr5eUGCy6axp3fQI9cNkrMG3zWxWQ3NW74jorZZOw/rYC1ZwKNIgsuuRLddSW5HvQlYAAAANffqEziG1WJRgwSH7rikj267qLeWb8iRxxvU7v35unR0F11xVjet3XxAh/K8Sk5y6uSOTWWaZrUFLNM0VegLyuMNatWm/QqGwmqf1lBtmjdUi5Zp2puzR9LRO1mR3MUqsme/R2lNk2JVNgAAAKKAkIVaJcFllyQN73f8Utn9ujWLdzml8gdC2n+4UK9MXqlVm/aXOJbWNFFXjummHh06S1KlV5lz2LkbBAAAUNNxxQZEUSgU1t6DHt357PzjApYk7dpfoKcmLtd3a/bKHwzLajHUPi2yDXydDqs6HbNiHQAAAGomQhYQRcFQWE9OXKbCCpZbf/2z1fL5w7JbLRo7PLINfIf3bVWpDZgBAABQPQhZQBRl783X9j15FbYLm9LUBVkKhsIa2quV+nZJLbd9iyYJuubck+Wu4RsSAwAAgJAFRE0gGCreyysS36/do9D/75304DUD9Juh7eW0l1xq3mJIA05qoWfvHC63s2YuQw8AAICS+FgciJJwWPL5yx8meCyfPyTDOLpLssth01W/6aGrftND363epZyDHjVMcGhYn1ay2yzFC34AAACg5iNkAVFis1nUonFCxO1TU9wKh3+eZFU0FHBU/zZRrw0AAADxw3BBIEqsFkOjB7aV1WJE1P6coe0ZAggAAFAHEbKAKPF4AzIshkb1P34Pr19q3jhBp/ZoIYuFH0EAAIC6huGCQBV5fUFl7czV5NkblZvn1V9vGKKDR7xavmFvqe1TU9x64uYhMiK74QUAAIBapk6ErC1btuiVV17R8uXLdeDAAbVo0UJnn322brzxRiUkRD5H5pcOHTqkc889VyeddJJef/31KFaMusLrD+qjWRmaMndT8WN/e2eJHrxqgFZn7df0xVu1bssBmebRZdjPGdZBowe01r6cHDVplFbl1/d4AwqHTW3ZdVihsKk2LRrK5bDK7bQVL6oBAACA+Kr1IWvVqlW66qqr5PF41KtXL/Xs2VM//vijXnvtNc2dO1cffPCBkpKSKn1ej8ej22+/Xfv27YtB1agLgqGwvlu1u0TAkqQNWw/ppqdna1T/1rrlwt5qlZooSQqFTUmmMjM2yOfzKT39xEOWPxDSkQK/3vxitZas2fP/55YMQ+rVKVXXnneSWjZNlMtR63/EAQAAap1afQUWDAZ19913y+Px6PHHH9f48eMlSV6vV3fddZfmzJmjZ599Vg899FClzpudna277rpLq1evjkXZqCNCIVMfz84s9ZjHG9RXC7foq4VbZLEYOr1PK918YS857RY1bNhIDRqlKBiSAqGgzLApdyWWaA+Fwtp/uFD3PP+t8gsDJY6ZprRy4z7d/fy3+usNg9S1bePj9t4CAABAbNXqWffTpk1Tdna2Bg8eXBywJMnlcumJJ55QQkKCPv74Yx0+fDii83m9Xr3xxhsaO3asVq9erdatK17AAPVXziGPduzNr7BdOGxq4cqdslktCoWl7QcNPTXpJ133+Czd/PRsvfbZamXn5Mkb4R5bwbCpx95eclzAKtEmFNbf/r30aOoCAABAXNXqkDVnzhxJ0ujRo487lpKSooEDByoQCGjBggURnW/69Ol65plnZLPZ9Pjjj+uWW26Jar2oWw4eLoy47Wl9WqnQF9Sdz83X05OWa92Wg8rN92l/rldzfsjWLX+fo4nT1kW0mfHmHYcjCneFvqDm/bhDwVA44joBAABQdbU6ZGVmHh2q1bVr11KPd+rUSZK0YcOGiM6XnJysW265RbNmzSpxZwwoTUKEQ/yS3HbdMLanHnh1kbJz8spsN3XhFn0+P0teX9lBy+sLas4P2yOucd6PO+TzhyJuDwAAgKqr1XOy9u49ukR28+bNSz3erFmzEu0qMmrUKI0aNSo6xZ0A0zQVCsX/gvjY16yO16+t2rZsqAYJduV5yh62J0lnnNpGy9bnlBuwikyZu0ljh3cs8+sQCoflKSeE/VKhNyiper6vqhPf0/FBP8cPfR0f9HN80M/xQT9XjVnFKRe1OmR5PB5JR+dglabo8aJ2NV1hYaFWrFhRrTWw2Efk0tLb6lcD2x63uuAvjerfWq9/tiqicxb6glq4cqe6tLCU+uFAy1atldLAGXGNKQ1d8ngKtWFd+TXWZXxPxwf9HD/0dXzQz/FBP8cH/Rx/tXq4oNV6dNW0ivYDqmoSBUpzcH+OLj6js7q2TSm3XWqKW7v2FUR83u05+bLaSh+KWJB3WL8e1Dbic501sI08BZEt/AIAAIDoqNV3shITE5Wbm6vCwtIXIPB6vZIkt9sdz7JOmNvtLnN+WSyFQqHiTzh69uxZHF4Rmb/dNETv/W+9vlm6XQXen4fyNWnk0tjhHWW3WmSzRb4xsM1qUfNmqWreLLXU4/6gqV6dmmrVpv3lnqd54wT169ZcVouU3qpVxK9fF/A9HR/0c/zQ1/FBP8cH/Rwf9HPVZGRklJkxIlGrQ1azZs2Um5urffv2lbrcetFwq6K5WTWdYRjV/gNgtVqrvYbaxmqVfvvr7vrdmB7K2HZQ+Z6AmjRyqX1aI0lH97Xq3TlVs5dlR3S+QSe3KPdr4LZKD1w9QH96eYG27yl9nlfjhi49fvNQWS2GrNZafcO6yviejg/6OX7o6/ign+ODfo4P+rnyKhopV5FaffVVdNdn06bS55sUPV4dd4dQv7icNjntVvXqlKohvdLUtW1jOexWOexWuV12XTiyc0Tnad28gdo0b1BhuwSnTc/84XRde+5Jat44ofjxRkkOXXxGZ71y30g1aeSq9wELAACgOtTqO1nDhw/X1KlTNWPGDF188cUljh06dEhLliyR3W7X0KFDq6lC4KjUFLfOPa29pi7YUmYbu82iuy/rF1EwslgMuRw2/WZYe40Z2l7+QEimKbmcVoXDppyOWv2jDQAAUKvV6o+5R48erbS0NC1cuFDvv/9+8eNer1cTJkyQx+PR+PHj1bRp0+JjgUBAWVlZysrKUiBQ/tLbwC+FwmF5vAH5AyF5/UEVeiP7HnI5bLpqTA/97uxuSnAdH4DSmyXp77edptbNk2SrxN0nu+3o3bKkBIcaJDpkt1kJWAAAANWsVl+NuVwuPfXUU7rhhhv06KOPasqUKUpPT9dPP/2kvXv3qkePHrr33ntLPCcnJ0djxoyRJM2ePVvp6enVUTpqmXDYVDAU1vdrdmvqws3ata9ANqtFfbqkavyozkpNcctVQbhxOmw6d1gHnXdaBy1etUvbcvJls1o06OQWatO8gaxWS6UCFgAAAGqmWh2yJGngwIGaPHmyXn75ZS1dulSbNm1Senq6xo8fr2uvvVZJSUnVXSJquXDY1JECn/708kLt2l9yKfY5P2Rrzg/ZOv/0Dvrd2d0rvIvksFu0Zs0adW7RWKf16Sar1SKLpWoTKwEAAFCz1PqQJUldunTRiy++GFHb9PR0ZWRkRNR23LhxGjduXFVKQx0QDIVLDVjH+uLbzUpOcuqcYR3kcpb/YxUMBrV3716lpaURsAAAAOogxiYB5QiFw/pu9e5yA1aRT+ZukkFoAgAAqPcIWUA5fP6Qpi7YHFHbgsKAlq7dE+OKAAAAUNMRsoBy2KwW7dqfH3H7rbuOKBQ2Y1gRAAAAajpCFlAO01Qll1Q3xIhBAACA+o2QBZQjFA6rd+fUiNsPPLmlDIOUBQAAUJ8RsoByJLjsunBU54jatmvZUC2bJsa4IgAAANR0hCygAi2aJOicoe3LbeO0W3XP5f1kZzNhAACAeq9O7JMFxJLLYdPV5/RQckOnPp27SR5vsMTxdi0b6p7L+6ll00RZa2nICoXC8vpDkiTDOHoHDwAAACeGkAVEwOmw6fzTOmrs6Z30/Zrd2rr7iOw2iwb3bKmWTRNlt1pqZcDyB0IyJS1euUvzf9qhQl9QKQ1c+s3Q9uraNkU2q4UNkwEAACqJkIVayeMNyGa1yDAMBUNhOWyxDzku59Efl+H90jUsFJZhMWSpxYtc+PxBbdpxWH/79xLlFwZKHFu0apdaN2+gv900RI2SHLJaal+ABAAAqC6ELNQqXn9QO/bma8rcjVq1cb8CwbBapSbp3NPaa2jvVrJbDVniEAhq412rY5mmqd37C/SX1xcrEAyX2iY7J0/3vfitXrp3pBJctfv9AgAAxBNXTqg1fP6g3vx8je56br4WrtilIwV+FfqC2rQjV899+JNu/+dcHS7wy+sLKBQKq9AXVCgUlscbqPjk9YzXF9KbX64pM2AV2XuoUFMXbJY/EIpTZQAAALUfIQu1QqEvqE/mbNTMJdvKbLN7f4H+9PJChU3p6sdm6uIHp+miB6fptU9XKTsnT15fsMzn1jceX1CrNu6PqO30xVtjWwwAAEAdQ8hCrWAY0hffbq6w3e79Bfpu9W6N6t9akhQIhjV3+Q7d9o85WrxqF0Hr/23dfTjitgePeCu84wUAAICfEbJQKyxeuUuFEQakGd9v04h+6SUeC5vSC//9STv25ceivFrHUOUW7KjF63sAAADEHSELNV4wGNa2nLyI2+/eX6CUBq7jHg+b0kczM5ijJaljeiNFsjK7YUhnD24n0zw6ZHPn3nztOVAgfyAkH/O0AAAASsXqgqjxLBZD9kqs5me3WRQIlT68bdn6HBnclpHdZlG/bs31w/qcMttYLYbuvKyvOqQ10sufrND3q3crGDIlSckNnPr1oHYaN7KTnHYre2kBAAAcgztZqPEsFkNDeqVF3L5v11Rl7cgt9Vg4bCrP449SZaXzeAMqKAwovzBQY+cyJbjsumFsT7mdZX/OctO4XmqQ4ChezbEoYElSbp5PH83K0L0vfhvxME4AAID6gpCFWiEtNVFtWzSIqO2Yoe01ffGWMo877dZolVXMNE35AiFt2HpQL09eqYff+E5PvrtUX3+3VYW+oLz+mhdEGjdy6e+3n6bUZPdxx1qlJmnQyS311H+WyV9OUNy+J0/PfvgjQzABAACOwXBB1Ap2q0X3/ba/7nnxW/n8Zc8FumBER0nSisx9pR5vn9ZQTkd0Q1Y4bKrAG9DDb3ynjdm5JY6t2rRf/5m2Tndd1lendGsuu63mDKtz2q1qlZqo1+4/Q2s3H9Dc5dnyeINq3NClS3/VVbOXbZe3nL4usmzdHvkCISW47HGoGgAAoObjThZqBavVohZNEvXMHaerY3qj4443THTomnN6aMyQ9vrbv5fINEs5iaSxwztWan5XJALBsO5/ZeFxAauILxDS05N+0PqtBxUKl1FYNbHbrHLYrerTJVU3jeuluy7rp9+fd5JcDpvmLM+O6BymKX2zdLvCZXU6AABAPcOdLNQaTodV6c2T9NStw7T3UKGWrt2tQDCs9mmN1K9rM63YuE/3vbhAufm+Up8/pGdLDe2VJmslQpYvEJKho5v3SlKC0ybTNOV02OTzh2TK1Pwfd2j7nvJXPzRN6c0v1ujZO0+P+LXjyTCMEneifP6QjhREPnftcL5PoVBYFlv0h2ICAADUNoQs1CpWi0VWh0VtmjdQm+YNZJpm8WqBvTo11Yj+6Zr1/TYVeH+eA5Wc5NS5p3XQ+ad3kNMR2bd8KByWzx/SZ/OyNOP7rTqU5ys+168GtdG4EZ2Vm+eVw27VVwvLnv91rOycPO3cm69GjRrp8OHINwOuDuFwWA0THMrNKz2w/lKDREelwisAAEBdRshCrXbscuwuh01XnNVNv/11d23YelB5Hr9SGrjUuXWyTFMRz8UKh03lFfh174sLlHPQU+JYbr5PH3+zUXN+yNbjNw1Vk0ZubdtzJOJ6N2bn6qR0Z8Ttq4vVatGIU9I1cfr6CtsahjR6QFtZWBofAABAEnOyUMe4HDY57Vb17pyqYb1b6aQOTeSwWyu12EUgGNbDb35/XMA61v5crx5/d2ml6zOMoysR1nQOu1VjhrSPaCXGfl2byRXlxUQAAABqM0IW8Avb9hzR5p0VD+fbvidPeR6/urRJifjcJ3VoIp8vsiF41c1mNTTh9wNkK2cYYKvUJN3721NYWRAAAOAYhCzgGIW+oKYv3hpx+x835Oj80ztE1LZz62Q1aeTWkSORDy+sTk6HTT3aNdHzdw/XoJNbyGL5eThgw0SHLhzZSc/eeboSnAQsoLYKhcLyeAPyB0Ly+oNsLg4AUcKcLOAY4XBYuXneiNsvXLlb9/72FPVo31jrthwss53NatEtF/aW3Vq75i05HVa1bdFQd13WT6Yp7cv1yGa1qFlKgsKmKVeEC4kAqFlCobCCYVOLVu7UtEVbtedAgZx2q07p1kwXjuys5AZOuZz8fAPAieI3KHAMwzCU6I78zozFIm3fc0QPXDVAL01eoaVr9xzXJrmBUw9cdarSmyeptq4NUTQcMNF9/B5lAGqXUCisQ3k+/enlBdp7qLDEsa+/36avv9+my0Z31bhRnfggBQBOEL89gWO4nTaNPKW1vv1pZ0Tth/RK04IVu7RpR65uubC3rhrTXbOWbtfeQx457VYNOrml+ndvLunoYhKhUCiW5QNAhQKhsP748gLt+0XAOtaHszKU0silUf1bR7QATm1hmqYKfUE57VYZhiFfICSH3SKrhdkTAKKLkAUcwzAM9ezUVKnJbu3LLfsCRDp6h+rU7s31+merVVAY0K3/mKOTOzbR6X1aqXu7xrJYDPXt0kyOOnSBAqB2CwbDmrMsu9yAVeSjmRk6o3/rOFQVH75ASGuy9uvTeZuUsfWQwqapdi0bauzwjhp0ckvZbQQtANHDbxTgF8LhsCZcM0AJrrI/g3A5rHro2kFauHKXCgoDxY+vyTqgV6es0rRFW3Ryhyb80QZQowTDYU1bFNkG6gePeLV284EYVxQfXn9Qf/v3Ej3y5vdatXG/fIGQAsGwNmbn6h/vLde9L35bYhN7AKgq7mQBv+B22tWscYL+fttp+mDGBi1Zu0eh8NG9rSwWQ6d2b67Lz+qm/EK/Tu3RXC/cPULfLN2u/MKAUho6dfbgdmqU5JSbSeMAahiHzaqd+/Ijbr919xH17doshhXFXqEvqBf/+5NWZO4rs82WXUf08Bvf6Ymbh8owjFqxnyGAmo2rQKAUNqtFWbsO69zTOuiGC3oW75vVLq2R9ucWasrcjfr2p52645I+Gt43XVed00OhkCmb1WB4IIAayzRN2WwWhfyRzQ912Gv/3XiPN6BFq3ZV2C5z+yFt3X1YycnJOnToUBwqA1CXEbKAUridNg3t2VKfzNmk1z9brdQUtyRpzwGPsnPy5HbadMfFfXRa71Y/hyq2iwJQw/kCIfXunFrqSqilOaVb8xhXFFv+QEjTFm1RpDemPv82S1f/uhMhC0CVEbKAMjgdNo0b2UkXjOiob5Zu16YduercOlkXjuykYb1byTRN9pEBqkGhNyAZhn5Yt0cHDnuV0tClASe1kEzJXc5cShzdjuHCkZ0iClk92jdWoyRnHKqKnUAorN37CyJun3PAI5uN7yEAVcdvEqAcRfOqxgxtL38gJMM4OqfBaq39Q2iA2sjrD+rfX63T3B+y5Qv8POTNYbNoeL90XT+2J/MhK9CxVbJGnpKuuct3lNnG7bTpD5f0rfXLt1tkyG6L/D047FbmYwGICq4UUS8U+oIKhcMKhcIKh4/uk1IZNqtFCS673E47AQuoJj5/UA+/8Z2+/m5riYAlSf5gWLOWbteEfy2S188qceVxOqy6dXxvjR/VWS7H8QGkY6tGeu6u4Wqa7JbFUkt3UP9/TodVQ3u3jLj9wJNayOf1xLAiAPUFH/ehxvL5j945Wrv5gHbuy5fbaVP/7s1ltx0NPJHw+oI6nO/TJ3M3av6PO49uQumwaljvVrrojM5q0sgll4MfA6CmCwRDmr54q9ZtOVhuu43Zufp8fpbGjejEIjTlcDpsuvjMLrpkdBctXLFLO/bmyWG3aljvNDVrnCC7rW5s0Fu0X2FyklO5+b5y29qsFp01qK02b8qIU3UA6jKuLlEjef1Bff3dNk2Zs7HEH0aLIZ3SvbnuuLiPkhIcspVzV8nrC2rJ2j167sMfi5dgl46Gt9nLtmvuD9t184W9NaJfOnOrgBrONKWvFm6OqO3/Fm/VuBGdYlxR7Vc0rPLMAW0UDIVlsRiyGLX7zlVZ/vi7/nrojcUKhsoeCnj7xb1lGJLPV34YA4BI1P6PqVDneP1B/WfaOr395ZrjPnkMm9KydTn6w7Pzle8JlHGGo3bsy9ezHywvEbB+ea5Xp6zUxuxchctoU98VDa0MBEPyeAMKhsLyVnKoJRANufk+7T1UGFHbg0e82pcbWVscZbNa6mzActit6twmWU/eMkztWjY87niLJgmacM0ADemZJgcbyAOIEj6+R42zfU+evlq4pdw2B4949eLHP+neK04pdeigxxvQBzM2qKLsZJrSBzM26KHrBjFZ/hd8/qBWZx3QlLkbtSbrgKSjn3yP7J+ui0Z1UYMEh5ylzOcAYsEfCFeyfWT7QKF+cDls6tQ6Wf+84zTt2l+g1Zv2KxQ21bVtijqmJ8tiSHabVaEQ3zcAooOrStQoHm9An8zZGFHb5etzFAiWfeG1fH1OROdZs/mAvP4gIesYPn9Qr3yy8rjVxwp9QU1ftFWzl2br4esGqUvblFq/+hhqh5QGTlkMVfjBiSQZhtSkkSv2RaFWsVktslktap/WSO3TGlV3OQDqOO6Lo0ZxOmz6IcJwFDalZWW0zc3zRXQxVuTgYW/kjaOgUaNG8gXCCobCyvf45Q+EVOgN1oilg33+oD6fn1Xu8s6+QEh/fft7ebzlD9kEosX4//mYkejdObXc+ZoAAMQaH92jXKZ5dE6OLxDSngMe2awWtWmepLCpmNz5MQyVe3fql8qaH1TZVcXiOeytTbsO8gYseu3T1Vq0cqf8//9+e3VuqvEjO6t7+8bVuuKhYRj6ckHFCwz4/CF9Pi9Ll5/VVU5WaESMJbjs+u2vu2v5hr3lzqG0GNIVZ3XjzjQAoFrxVwhl8vqD2r4nTxOnr9fKjfuKH3c7bRp5Srp+N6aH3E5rVJf5DQbDSk12RzxpPS01qdTHGyQ61LJJonYfKKjwHI0butQsJaFSdZ4ofzCsDdkFeub9H49bkGPVxv1atXG/xgxpp2vOPanagtaazQd0pMAfUdtZS7fpd2O6x7gi4Ki01ETd99tT9Mz7y0tdJc5iMXTnJX3VrmVDGXV0EQcAQO3AeAqUyusPakXmPt330oISAUv6/3k5i7fqzmfnqaAw+kPczhrUNqJ2yUlOndyhSanHLIah807vENF5zh7SLtLSquzgYV+pAetY0xdv1dwfshUIVs8E7L0HI9+IM88TEJeyiBeX4+heeW8+OFrnndZBDRMdkqQkt12/GdpObz54pgb3asmWDACAasdfIpQqEAzr6Yk/lDssJ+egR/947wc9eNWpcke4OXBFHHarzhnWQV98m6W8CpZoHzeyk8rKd3abRaMHtNF3a3Zr1cb9ZZ6ja9sUjR3eMS6blnq8Qf33m8xyA1aRKXM36YxT28S8ptIkuiP/WtqsBncMEFcuh00uh02/O7u7rjnnJFmthkJhU8FgmHAFAKgxuJOF4/gCIX21cLOCoYrnRq3I3KcCb3T3TXLYLXr85qFKKudi/5yh7TVmSLty51I5HTb95fcDNfb0DsfNz3A6rBozpJ3+dtOQuA3Lc9gtWrRyZ0Rtcw56lJ2TF+OKStevazPZrJEFp4EntZTXz75ZiD+X0yabzSLDMGSzWghYAIAahb9KOE44bOrbnyILA5I0e9l2XXRml6hsZOkPhBQIhtW8cYLemjBaXy3crBnfb9O+3ELZrBad2qO5LhzZWW1bNohosQWXw6Yrft1dvz27h1Zu3KeDR7xqlORU366pMk3Fdd6T1x8qXuQiEvsPF6pjenLsCiqDYUhDe6VpfgTfA+PP6FzqPmUAAAD1GSELx7EYhgoKI1+aO88TUCgUlsV24kPuCn1BBYIhTVu0RZt2HJbFkAad3FLnntZBF4zoJJvNIplH54pV9qK+6BPuASe1OOH6osFhq9yN4+oKLwkuu26+sLc27Tisnfvyy2x38Rmdld6s9IVHAAAA6jNCFo4TCofVKMmpQ3m+iNo3buis0p40Pn9Q73+9XlMXbikxB+z7NXv08uSVunJMN509pL1cDlutvmsSDIXVu3PqcQuJlCbRZVOXNilxqKp0bqdNz/zhdL315Rot+GmnfIGfF+Fo2SRRl4zuoqG906p1qXkAAICaiiskHMdus+jMAW301hdrKmxrMaQzB7Q54cUPCn1BTZy+Tl8t3FLq8WAorH9PXSfDMHTWoHa1eu8bt9OmC0d2iihknXFqG5mV2U05yiwWQ4luu24Y21M3jO2p9VsPyusLqnnjBKU3byCLYcheyTtzAAAA9QVXSTiO3WbV6AFtlOCqONAM7plWpbtYnsKApi0qPWAd673/bTjh16gpDMNQ93aNNaaCJeM7t07W78Z0rxET+d1Om9xOm/p1baYhvdLUMT1ZTruVgAUAAFAOrpRQKpvVokdvGCxXOav3dUpP1h8u7XvCQ/i8/qA+n7+pzGXYj+ULhDR72faIVjysyew2Q7/7dVfdNK7n/7V353FRlfsfwD+zwYCAiAgqS6IyCIQpmebyE8HUrNywRXO7XU0rW37+rpn3Wtqva2ldf/a6BeXN7tWbqbhRXvcFt5DUVHypyJIbICqbDGKsM/P8/qCZwhlgwGEW+Lxfr17VeZ5zeObDMvM9zznPga9X3Qcgt1PKMfa/uuOj1wbD2QpLyhMRERFRy7D9qXKyS04KGYK6tscXC2KwcX8mjv3uvhxfL1c8MzgITw7q9kD35Gi0Apk5arP7p1+/g5h+AQ80c2YPrlzORJ8gXzzRPwY38stQVFqBdkoFggM7QOgE73MiIiIicnD8NEf1clLI0KmDK14eH4E5sb1R9ks1ZL/eqyOR1F5W+KAEzL/vyJwZL0eg0Whw+1YeOvt2Qg9/T5ss005ERERELYdFFjVKv9iEs6dLne0arQ7Vv85uOSlkTZ5hkksl6OnviYzrJWb1Dwn0hJMFCjsiIiIiopbEIouarLJKA0iAw2du4NLVYggAIYEd8ET/QAAwewVApbMc46N61ruy4O8p5FKMGPBQ7fOyiIiIiIjsGIssapLKag0OnMrBv3dfQlX1b89OOnr2BtbuTMMLI0Mw9r+6m31fkUc7J4zoH4gDp3Ia7Pf8E6pmLxNPRERERGRNLLLIbBVVGuw/mV3v87OqNTqs250OjVaHCVE9zZrRcnGWY86E3pBIgAOncozuu5JKJXh+eDAmRPWAswMvCFFZrYFWKyAg4BfQDXfVxbYeEhERERG1EMf91Eo28e9dlxrts+lAFsYM7g44m3dMZycZZo2LwIujeuH7Y1dwNa8UEkgQFuSFMUO6Qy6XOmyBVVGlQUWVBtuPXkH69TvQCYGgru0xfmgQqjUCLrzFjIiIiKjVccxPrmR1NRodDp7KQY2m8edU6XQCO49fxcToYDiZ+bwn/UNvp4zq9euMD6CQSeHcwHO67F1VtQYb92caPQssM7sEe3+8jqi+fnjj+b4O/RqJiIiIyBhXESCzVNdoceFKkdn9067eQbUZBdn9lE5ytHNRwM1F4dDFR0WVBpsP/ozvjtT/sOWjqXn4fHNq7UIiRERERNRqsMgis+l05j+oqil9WyOdTiDxyOVG+x1NzUNJWZUVRkRERERE1sIii8yikEvR3a+92f2DunpALmubqwHWaHTYdzIbGq15M3nfHb2MCs5mEREREbUaLLLILE4KGZ4ZEgSpmXXT+GE9zV7GvbWp0WhxOVdtdv+reaVtfuaPiIiIqDVhkUVmU8hleHpwUKP9hvcLgJuLwgojsl9NeaSXVCIBSywiIiKi1oNFFpnNxVmOGU+HY/SgbvX2GRbpj1cnPmLWM7JaK2eFDBE9vM3uHxrk1WYvrSQiIiJqjdruJ2FqFmcnGV56JhzPxQTjuyOXkZmjhhACPfzbI3ZYMDq4Ozv0qoCWIJNJMexRf/zzPxdRWa1tsK9EAowb2qPNXlpJRERE1Brxkx01mf6ZVtOfCoP213uJpFJJm569up8QwEvPhOPLxPMN9hsf1ZO5EREREbUy/HRHzaZkcVAvF2c5Yh4LAACs2ZlmNKMll0kRG90Tzw8PhjNnsYiIiIhaFX66I2ohSic5ovsFIOaxABw5cwMXrxRBJ4Aefu0x8vFAyKRSFlhERERErRA/4RG1IP2lgCP6B2JoXz8IIaAuKUb21Z8RGhpq49ERERERUUtgkUVkBTKZFK4yKbRaLW7fumnr4RARERFRC+IS7kRERERERBbEmSxyGFXVGkgkEqjvVUEIoIO7M3Q6wQU4iIiIiMiu8NMp2T2tVoeKai22HMzCgVPZKCuvAfDrCn79AjB5ZAjaKRWQyzkxS0RERES21yqKrGvXriE+Ph5nzpxBcXExOnfujNGjR2POnDlwdXVt0rHy8/PxxRdfICUlBbdv34a3tzdiYmIwd+5ceHl5tdArsA9Sqf0VKTqdQOkv1Zj/92MoVFfUaauo0mDX8Wv44Vwe/vbmf8GngyvkMvt7DURERETUtjj8J9Lz588jNjYWO3bsgLe3N4YNG4by8nKsWrUKkyZNwr1798w+Vm5uLiZOnIiEhAQolUpER0dDJpPh22+/xYQJE3D79u0WfCW2U6MViOj9CMLCIwBIUFGlge7XhwzbWo1Gi/dX/2hUYP3e3V+q8e6qFLsZsyXVaLSortGiRqODRqtDZbUGWq3O1sMiIiIiogY49EyWRqPB//zP/6C8vBwffvghnn32WQBAZWUl5s2bh0OHDmHlypVYvHixWcdbuHAhCgsLMXfuXLz55psAAK1Wiw8++AAJCQlYsmQJ/vGPf7TY67E2jVaHqmotdiRfxZ6U67hztxIAENrNCxOieyJS5QNnJ5lNx5iTX4ZrN+822q+wpALnLxchMsQHUqnECiNrWTqdDtUaHfafyMaO5Ku4XVwOAAjq6oHxUT0w+BE/OCts+70hIiIiItMceiZr165dyM3NxcCBAw0FFgAolUp89NFHcHV1xebNm1FaWtrosU6fPo3Tp0+jW7dueP311w3bZTIZ3n33XXTt2hVHjhzB5cuXW+S1WJtGq0PBnXK8sjwJ6/dmGAosAEi/fgcfrTmF5d/8hKpqrc3GWFmlwd4fs83uv+/EdVRUaVpwRNah0wnc/aUab644gtXbLxoKLAC4dvMuPt2Yir98cRzllTU2HCURERER1cehi6xDhw4BAEaMGGHU1qFDBwwYMAA1NTX44YcfzD7W8OHDje5NUigUiImJAQAkJSU96LDtglarw8L4ZKjvVdXb53R6Pv6146LNChetTuDuL/WP734NvRZHotHqsOjLFNwq/qXePlk5Jfjbt2daRVFJRERE1No4dJGVlZUFAAgJCTHZ3rNnTwBARkaG2cdSqVQPfCx7p9XqcPTsDZSUNV6UHDiVAyFsc6+TVCpBOxcns/u7N6GvPUu/fgc5+WWN9judno+y8morjIiIiIiImsKh78kqKCgAAPj6+pps9/HxqdPPWsdqLiEEtNqWvzyvqkaHPWZehlej0eHImRt4cuBDVi+2nBVSjOgfiKSfcszq/0T/QDg7Sa2SYXP9fmymxllZrcXO5GtmH29X8lVMHtULCpnj34dmSY3lTJbBnK2HWVsHc7YO5mwdzPnBPOjnXocussrLa+9VUSqVJtv12/X9zDmWi4vLAx+ruSoqKnDu3LkWO75eWHgEihpYre9+t++U43Z+AW7dzGvBURmTSCQIDXsYfp3ckFfY8CqRnu7OiAzphIsXLjjMH5ILFy4YbQvqEdKk702BugJqdSnycq9bcGSti6mcyfKYs/Uwa+tgztbBnK2DOVufQ18uKJPVrq4mkTR8Ft+cStSSx7J3Gq2uSasGKp1kgLD+suFCCOTfvoXFM/vDo139lwK6OMuxZOYAFBYWOEyBVR+dTlebt5mUTnLodFzSnYiIiMieOPRMVrt27aBWq1FRYfrMf2Vl7Yp59c1O3X8sABY5VnO5uLjUe3+ZJVXX6PD4w12w/dgVs/pHRfrD18ul3kspW5pWJxD3djT+vSsdyefyUFVTW0jJZRIM6t0V058Kg6ebE+QyD3Tt0tkmYzSXVqs1nE2KiIgwFPeGdp3A4Ee64uLVYrOOF9XXD76dvODbqXU/KLupGsuZLIM5Ww+ztg7mbB3M2TqY84PJzMysty4wh0MXWT4+PlCr1SgsLERAQIBRu/7+Kf39VI0dKy0tDYWFhSbbm3Ks5pJIJFb5BXCRyTBhWA/854craGxiLuShDvDyUNr0F1MmA5wUcsyZEIFXJkTgVtEvEAA6d3QFALgqFTYb24OQyWRGucpkwPDHArF25yVDMVmfTp4uCAvqyD+ajTCVM1kec7YeZm0dzNk6mLN1MOema+zqtsY49OWC+lmf+p5dpd9uzuyQJY/lCNopFZg9PqLBPh7tnPDOtH5289BbF2c5lM5yBPm1R3e/9nBVKhy2wGqIRAK8PfXRBh+q7Owkw6I/9scD/v4TERERUQtw6CIrKioKALBv3z6jtpKSEpw8eRIKhQKDBw82+1gHDhwwuselpqbG8Hys6OjoBx22XVA6y/HEY4H484zH4NfJrU6bVAI8FuaLv/9pGDzdlQ1+2CfLUzrJ8YiqE5a+Mgg9/NobtUf08MbKt4bC38cNCrl9FMBERERE9BuHvlxwxIgR6Nq1K5KTk7F+/XpMmTIFQO39U4sWLUJ5eTkmT54Mb29vwz41NTXIyaldEjwwMBAKRe1MSGRkJCIiInDhwgWsXLkSf/rTnyCRSKDVavHhhx/i1q1bGDp0KEJDQ63/QluI0lmO/uGd8WgvH2Tfvovc/DI4O8kR0cMbCrm0Vc4SOQqlkxxh3byw/PUhKCqpwJW8UkilEvTq5gU3FwWcFTIWv0RERER2yqGLLKVSieXLl2P27Nn44IMPsG3bNvj7+yM1NRUFBQUICwvD/Pnz6+yTn5+Pp556CgCQlJQEf39/Q9uyZcswdepUrF69GklJSQgODkZ6ejpycnLg5+eHpUuXWvX1WYNcJoUEAtLqO+jpo0BAQBdIpQ49wdlqyGRSyGRS+Pu6w9/X3dbDISIiIiIzOfyn6QEDBmDLli0YNWoUbt68iSNHjsDd3R2vvfYa1q1bBzc3t8YP8qvg4GAkJiYiNjYWZWVlOHz4MABg2rRp2Lx5s81W17OG0tJSFBUVtYol6omIiIiIbMmhZ7L0VCoVPvvsM7P6+vv7IzMzs952Pz8/LFu2zFJDIyIiIiKiNsbhZ7KIiIiIiIjsCYssIiIiIiIiC2KRRUREREREZEEssoiIiIiIiCyIRRYREREREZEFscgiIiIiIiKyIBZZREREREREFsQii4iIiIiIyIJYZBEREREREVkQiywiIiIiIiILYpFFRERERERkQSyyiIiIiIiILIhFFhERERERkQWxyCIiIiIiIrIgFllEREREREQWJBFCCFsPoq07d+4ctFotJBIJXFxcrP71hRCoqKgAALi4uEAikVh9DG0Fs7YO5mwdzNl6mLV1MGfrYM7WwZwfTEVFBYQQkMlk6NOnT5P3l1t+SNRUOp0OQO0vQ3l5uU3Hov9lpJbHrK2DOVsHc7YeZm0dzNk6mLN1MOfm039ObyoWWXZAoVCgpqYGUqkUzs7Oth4OEREREVGbVlVVBZ1OB4VC0az9ebkgERERERGRBXHhCyIiIiIiIgtikUVERERERGRBLLKIiIiIiIgsiEUWERERERGRBbHIIiIiIiIisiAWWURERERERBbEIouIiIiIiMiCWGQRERERERFZEIssIiIiIiIiC2KRRUREREREZEEssoiIiIiIiCyIRRYREREREZEFscgiIiIiIiKyIBZZREREREREFsQii4iIiIiIyIJYZBEREREREVkQiywiIiIiIiILktt6AGRb165dQ3x8PM6cOYPi4mJ07twZo0ePxpw5c+Dq6mrr4dnc9u3bsXXrVmRkZKCiogIdO3bE448/jtmzZ6NHjx5G/VNSUrB69WpkZGSgsrIS3bt3x6RJk/Dss89CIpEY9ddoNNi2bRs2bdqE69evQy6XIyIiArNnz8aAAQNMjqm0tBRff/01Dhw4gJs3b8LDwwNDhgzB3LlzERAQYPEMbOHNN9/Evn37sGzZMsTGxhq1M+fmu3PnDlavXo1Dhw7h5s2bUCqV6N27N2bNmoWBAwca9WfWzXP06FGsXbsWFy5cQGVlJXx9fTFs2DC8+uqr8Pb2NurPnM1z/fp1jB8/HrGxsVi8eLHJPvaapaO93zaWdUVFBdasWYN9+/YhOzsbOp0Ofn5+GD58OGbNmgVPT0+jfZi1MXN+pn/v3r17GD9+PHJzc5GUlAR/f3+jPszZPkiEEMLWgyDbOH/+PGbMmIHy8nL07t0bXbp0wdmzZ1FYWIiQkBBs2LABbm5uth6mTQghMH/+fOzcudPwx8nLywsZGRnIy8uDUqlEfHw8hgwZYthn48aNeP/996FQKDBgwAAoFAqcOHECFRUViI2NxbJly+r9Gu3bt0f//v2hVqtx5swZCCHw4YcfYuLEiXX2UavVmDJlCi5fvozAwECEhobi2rVryMrKgpubG9avX49evXpZJaOWsmXLFrz77rsAYLLIYs7Nd/XqVcyYMQMFBQXw8/NDWFgYbty4gfT0dEgkEnz22WcYOXKkoT+zbp41a9Zg+fLlkEgk6Nu3L7y8vHD+/HkUFBSgU6dO2LBhAwIDAw39mbN5ioqKMH36dFy5cgVTpkwx+YHUXrN0tPfbxrJWq9WYNm0asrKy4O7ujoiICMhkMly4cAFqtRp+fn7YsGEDOnfubNiHWRsz52f6fvPnz8eOHTsAwGSRxZztiKA2qaamRgwfPlyoVCqxZcsWw/aKigrxyiuvCJVKJf73f//XhiO0re+//16oVCoxePBgkZ6ebtiu0WjEypUrhUqlEgMHDhT37t0TQghx9epVERoaKiIjI0VaWpqhf15ennjiiSeESqUSe/bsqfM1tmzZIlQqlRg3bpwoKSkxbE9JSREREREiIiJC3Lx5s84+b7/9tlCpVGLhwoVCo9EYtn/55ZdCpVKJZ555Rmi1WktGYVVXr14Vffr0ESqVSqhUKrFt2zajdubcPDU1NWLcuHFCpVKJpUuX1nld3333nVCpVKJv376isrJSCMGsm+vGjRsiPDxcPPzww+LEiROG7VVVVeKtt94SKpVKzJ4927CdOZvn0qVLYsSIEYa/Daben+w1S0d7vzUn67/85S9CpVKJqVOniuLiYsP20tJSMXPmTKFSqcSsWbPq7MOs6zIn5/tt377d0F+lUonc3FyjPszZfrDIaqP0RcSMGTOM2u7cuSP69OkjwsPDhVqttv7g7MDUqVOFSqUSiYmJRm06nU48/fTTQqVSiX379gkhfnvDiY+PN+p/7NgxoVKpxMSJE+ts17/p//TTT0b7rFixQqhUKvHxxx8btuXm5orQ0FDRr18/UVZWZrTP888/L1QqlThy5EiTX689qKqqEhMmTBCRkZFi/PjxJoss5tx8u3fvFiqVSkyePFnodDqj9lmzZonhw4eL1NRUIQSzbi79B5w5c+YYtV27dk2oVCrxyCOPGLYx54ap1WrxySefiIcfflioVCoRExPT6Ad/e8vSUd5vzc26oqJC9O7dW6hUKnHjxg2j4xQVFYmQkBAREhIi7ty5Y9jOrGs15Wf693JyckRkZKSYMGGC4WSkqSKLOdsPLnzRRh06dAgAMGLECKO2Dh06YMCAAaipqcEPP/xg7aHZBQ8PD/To0QP9+vUzapNIJAgKCgIA5OfnAwAOHz4MAHUutdIbNGgQ3N3dceHCBUP/y5cvIycnBx07dsSjjz5qtM+oUaMA1F4KoHf06FFotVoMGjTI5BS8qX0cyaeffoq0tDQsXrwYXbp0MdmHOTffnj17AAAzZ840eV/K6tWrcfDgQfTp0wcAs24umUwGACgoKDBqKyoqAlD7N1aPOTfsm2++wddffw0vLy98+eWXGD9+fL197TVLR3m/NTfr4uJihIeHIzIyEn5+fkbtHTt2RPv27SGEMPweMOvfNOVnWk+j0WD+/PnQarVYsWKF4e/M/ZizfWGR1UZlZWUBAEJCQky29+zZEwCQkZFhtTHZk/j4eOzevdvkzZ5arRZpaWkAgC5duqCoqAjFxcVQKBTo3r27UX+ZTGbYnpmZCeC3/IODg01+4NVvz8nJQUVFRZ19VCqVyTE78vcsJSUFa9aswdNPP41x48aZ7MOcH8zFixcBAH379oVarcaGDRuwePFifPDBB9i9eze0Wq2hL7NuvoEDB0KhUCAtLQ1LlixBXl4eKioq8OOPP+LPf/4zAGD27NkAmLM5OnfujHfeeQf79u1DTExMvf3sOUtHeb81N2v9/VYbN2402Z6dnQ21Wg2pVApfX18AzPr3zM359+Li4nDu3DksXLjQ5M+3HnO2L1xdsI3Sn13S/wG8n4+PT51+9JsNGzYgLy8Pnp6eGDhwILKzswEA3t7ekEpNn7e4P8/G8nd2doaHhwdKS0tRVFSEgICAVvs9u3PnDhYsWIDOnTvj/fffr7ef/nUx56arrq5GXl4e5HI5MjMzMW/ePJSUlBja169fj7CwMKxatQq+vr7M+gF07twZn376KRYtWoSEhAQkJCQY2jp06IC4uDjD2V/m3LjnnnvOrH72nKWj5G9u1o1ZsWIFgNrZQ/0Kg8z6N03N+fTp0/jqq68QHR2NSZMmNdiXOdsXzmS1UeXl5QAApVJpsl2/Xd+Pav3444/45JNPANSu8NOuXbtGswRq/7ABv+X5yy+/mL2Pvq9+XxcXF5P9HfV7tmjRIhQXF+Pjjz+Gh4dHvf2Yc/Pdu3cPQO2qU6+99hpUKhW2bduGs2fPIiEhAREREbh06RJeffVVaLVaZv2AwsLCMHLkSMjlcvTt2xfR0dHw8fFBSUkJvvrqK+Tm5gLgz7Ql2XOWben9dtWqVdi/fz+USiUWLFhg2M6sm+fu3bt4++230aFDB3z00UeN9mfO9oUzWW2UTCaDTqczOZ38e4Ir/BscPnwY//3f/43q6mpMnjzZcDZKf9a0sSyB3/LUX09tzj565u7jSN+z9evX49ChQ3j55ZfrfXaHHnNuvurqagC1l7r6+/vjn//8JxQKBYDaywfXrFmD0aNHIy0tDfv37zecnWTWTZeeno6XXnoJzs7O2LJlC8LCwgAANTU1WLlyJf71r39h2rRp2L17N3+mLcies2wr77efffYZ4uPjIZVK8dFHH9W5lIxZN897772Hmzdv4quvvoKXl1ej/ZmzfeFMVhvVrl07ADBck3u/yspKAPWf2Whr1q1bh7lz56KyshJTpkzBkiVLDG36LPWZmVJVVQUAhofzNWUf/fegtX3Pfv75Z3zyyScIDw/HW2+91Wh/5tx8vz/bOHnyZEOBpefu7o4xY8YAqJ2tZdbNt3TpUpSUlOC9994zFFgAoFAosGDBAjz66KO4desWtm7dypwtyJ6zbO35V1dXY8GCBYiPj4dcLsfHH3+Mp59+uk4fZt10W7duxd69ezF16lRERUWZtQ9zti+cyWqjfHx8oFarUVhYaHJxB/11tPrratsqjUaDDz74AJs2bYJEIsG8efPwyiuv1OmjP+tfVFQEIYTJMzv356nfp7Cw0OTXraqqwt27dyGRSNCpUyez9nG079mKFStQWVkJpVJpWBBAT7+wyObNm5GSkoLHHnvMsMIRc246d3d3ODk5obq62ujBlXr67SUlJfyZbqaqqiqcPXsWEokEgwcPNmqXSCSIiorCmTNncPHiRYwdOxYAc7YEe/6Zbc3vt8XFxZg7dy5SU1Ph5uaGv//97xgyZIhRP2bddB9++CGA2jHPnz+/Tpu+iFm2bBlcXFwwadIk9OvXjznbGRZZbVRISAiysrJw+fJlREZGGrVfvnzZ0K+tqqysxNy5c5GcnAwXFxcsX74cTz75pFE/T09P+Pr6Ij8/H9nZ2ejWrVuddq1Wi6tXrwL4LU/9v3/++WeTX/vnn3+GEAKBgYGGs66N7eNo3zP9tdpnzpzBmTNnTPZJTU1Famoq5HI5XnjhBebcTDKZDMHBwUhLSzMsX30//fLiXl5e/Jluprt370Kn00Eqlda7xLJ+u0ajYc4WZM9Zttb325ycHPzhD39AXl4e/P39sWrVKgQHB5vsy6ybTv8euX///nr7HDx4EEDtIiP9+vVjznaGlwu2Ufqp53379hm1lZSU4OTJk1AoFCbPxrYFWq3WUGB17NgR33zzjckCS0+fp6k/hsePH0dZWRl69eqFzp07AwAeeughdOvWDQUFBTh37pzRPnv37gUADBs2zLBt6NChkEqlSE5ONnkzqf57GR0dbfbrtKV169YhMzPT5D/Dhw8HUHuWLjMzE8uXLwfAnB+E/jX+5z//MWoTQuDYsWMAYLg3jlk3XceOHeHp6QmdTocjR46Y7HP8+HEAQGhoKADmbEn2mmVrfL/Nz8/H9OnTkZeXh969e2Pz5s31FlgAs26O+t4fMzMz4e7uDqD22VWZmZmIjY0FwJztDYusNmrEiBHo2rUrkpOTsX79esP2yspKLFq0COXl5Xj22Wfh7e1tw1Hazpdffonk5GS4urri3//+N3r37t1g/ylTpkAmk+Ef//hHnT9sN2/exF//+lcAwJw5c+rsM336dAC1N7bqZxGA2ntivvnmGygUCvzxj380bPf19cWIESOgVquxZMkSw2IGAAxft2fPnoYCpTVizs03adIkeHh44NSpU4iLizPckCyEwGeffYaLFy8iICAATzzxBABm3RxSqRSTJ08GUHupj/5ZMgCg0+kQFxeHlJQUeHh4YOLEiQCYsyXZa5at8f327bffxq1btxASEoK1a9eiY8eOje7DrK2DOdsPiWjrS3+0YSdPnsTs2bNRWVmJ8PBw+Pv7IzU1FQUFBQgLC8O6detMPv27tSstLcWwYcNQXl6Obt26ISIiot6+Y8aMMZzR+eqrr/B///d/kMlk6N+/P5RKJU6ePIny8nI899xzWLp0aZ19dTodXn31VRw5cgRubm4YMGAAysrKcPr0aQghsHz5cqMnwRcUFODFF19Ebm4uunbtioiICFy7dg1ZWVnw8PDAt99+2yqm51977TUkJSVh2bJlhjN0esy5+Y4dO4Y33ngDlZWVCAwMNFzykZ2djfbt2+Prr7+uc0KBWTddTU0N3njjDRw+fBhSqRSRkZFo3749MjIykJeXB1dXV8THx2PQoEGGfZiz+T7//HPExcVhypQpWLx4sVG7vWbpiO+39WV9/Phxw4f0fv36oUuXLvUe46233jLcs8OsTWvsZ9qUfv36oaysDElJSUb32TJn+8Eiq43LyspCXFwcTp06hfLycvj7+2PUqFGYOXNmm/3lOHDgAF5//XWz+i5YsAAzZ840/H9SUhLWrl2LtLQ0SCQSBAUFYfLkyZgwYYLJB2TW1NTg22+/RWJiIrKzs+Hm5obQ0FDMmTMH/fv3N/k179y5gy+++AKHDh1CQUEBfHx80L9/f8ydO9fkDaiOqKEiC2DODyI7OxurVq1CSkoKiouL4e3tjSFDhmDOnDkmXxezbjohBBITE5GYmIiMjAxUVVXBx8cHgwcPxssvv4zAwECjfZizecz5QGqvWTra+219WS9duhTr1q0z6xhbt26tc6KSWRuzdJEFMGd7wSKLiIiIiIjIgnhPFhERERERkQWxyCIiIiIiIrIgFllEREREREQWxCKLiIiIiIjIglhkERERERERWRCLLCIiIiIiIgtikUVERERERGRBLLKIiIiIiIgsiEUWERERERGRBbHIIiIiIiIisiAWWURERERERBbEIouIiIiIiMiCWGQREZHDiomJQUhISIP/3L17t8nH+/zzzxvte/LkScPXuHHjRp22zz//HCEhIYiJiWn0OKmpqYbjzJo1y+yx/t7ixYsNx9i9e3ezjkFERJYjt/UAiIiImqOsrAx5eXmQyWR45JFH6u0nk8msOKqm27Ztm+G/k5OTkZubi4CAALP3r6ysxK5duwz/v3HjRjz11FMWHSMRETUNiywiInJIGRkZAICHHnoIGzdutPFomqeiogJ79uyBRCLBkCFD8MMPP2DTpk2YP3++2cfYu3cv7t27h4EDB+L06dM4deoUrly5gh49erTgyImIqCG8XJCIiBySvshSqVQ2Hknz6Quk4OBgjB8/HgCQmJiI6upqs4+hnwkbOnQoBgwYAABISEiw+FiJiMh8LLKIiMgh6Yus4OBgG4+k+RITEwHUFkjR0dFQKpUoLi7GgQMHzNo/NzcXP/30EwAgKioKTz75JABg+/btqKysbJlBExFRo1hkERGRQ9IXWSEhITYeSfPk5OQYCqSRI0eiXbt2iI6OBgCzL39MTEyEEALdu3dHjx49MGrUKCgUCpSWlta5T4uIiKyLRRYRETkcrVaLy5cvAwB8fHywdu1avP7665gxYwYWLFiA3bt3Q6fT2XiUDdu2bRuEEAgMDDQs3PHMM88AAH766SdcuXKlwf11Oh2+//77Ovt5eHggKioKAC8ZJCKyJRZZRETkcK5fv264HO6ll17CsmXLcODAAZw4cQLbt2/HvHnz8MILL6CgoMDGIzVNp9Nh+/btAIAxY8YYtkdFRcHT0xNA47NZKSkpuHnzJgBg7Nixhu36/z5//jwuXbpkyWETEZGZWGQREZHD0V8qCAARERFYv349zp07h5MnT+Jvf/sbOnXqhPPnz2P27NlNWkRCLy4urtHnb02fPr3Z409OTsatW7cA1C2QFAqF2fdV6Re86Nu3b50l36Ojo+Hh4QGAs1lERLbCJdyJiMjhdOnSBdOmTYNUKsXChQshldaeM3RxccHYsWMRERGBCRMmID09HVu3bsWLL77Y5ON36dKlwT737t1DVlZWs8avX/Cid+/e6NatW522sWPHIiEhAXfv3sWuXbswceJEo/1LS0tx8OBBQ//fc3JywqhRo7Blyxbs2LEDCxYsgJubW7PGSUREzcMii4iIHE5kZCQiIyPrbQ8KCsKYMWOwefNmHDx4sMlF1sSJE/HGG2802OfkyZPNms1Sq9VISkoCYFwgAcCjjz6KgIAA5ObmYuPGjSaLrJ07d6K6uhoKhQKjR482ah83bhy2bNmC8vJybN++HVOmTGnyOImIqPlYZBERUasUGhoKALhx4waOHj2KVatWmez3yiuvGBaLsIYdO3YYLmFcunQpli5dWm/fCxcuIC0tDeHh4XW26y8VrKmpweOPP97g10tISGCRRURkZSyyiIjIIel0Omg0Gjg5OZlsF0IAAORyOYqLi3H27FmT/YqLi1tsjKboCyRXV1e4u7vX26+goABCCCQkJOCvf/2rYXtGRgbS0tIAAN7e3pDJZCb3r6qqglqtRlZWFs6ePdvgzB8REVkWiywiInI4U6ZMQWpqKl588UW8++67JvvoC5GePXsiNjYWsbGx1hyiSRkZGUhPTwcALFu2zLDIhSlz5szBkSNHsHPnTrzzzjuG+6r0RZqnpycOHz5cb5FZUFCA6OhoaDQaJCQksMgiIrIiri5IREQOJzg4GFqtFvv378e9e/eM2vPy8rBnzx4AwFNPPWXt4dVr69atAIAOHTogJiamwb6TJ08GAMN9VQBQXV2NHTt2AKhd+r2+AguofX7Y8OHDAQB79+5FSUnJA4+fiIjMwyKLiIgczh/+8Ac4OTkhPz8f8+bNQ2FhoaEtIyMDM2fORHl5OR577DGMHDnShiP9TVMKJAAYOnQo/Pz8APy2FPuhQ4cMxZKpBTHupy/Uqqqq8N133zV77ERE1DQssoiIyOF069YNK1asgLOzM44dO4bo6GiMGTMGo0ePxrhx43Dt2jU8/PDDiIuLMyzvbmsHDx6EWq0GYF6BJJVK8cILLwAAsrKycObMGcOlgmFhYYaFPRoycOBABAUFAQA2bdpkuE+NiIhaln288xARETXRqFGj8P333+P555+Hj48Prl27hsLCQvTt2xfvvfceNm3aBE9PT1sP00D/bKzw8HD06tXLrH2effZZKBQKALUPSD5+/DgA84o0vUmTJgEArl+/jhMnTjRlyERE1EwSwdNaREREREREFsOZLCIiIiIiIgtikUVERERERGRBLLKIiIiIiIgsiEUWERERERGRBbHIIiIiIiIisiAWWURERERERBbEIouIiIiIiMiCWGQRERERERFZEIssIiIiIiIiC2KRRUREREREZEEssoiIiIiIiCyIRRYREREREZEFscgiIiIiIiKyIBZZREREREREFsQii4iIiIiIyIJYZBEREREREVkQiywiIiIiIiILYpFFRERERERkQf8PbWdqpSoKNaoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='5-HIAA', y='median_mmNorm', hue='VisitCode',\n",
    "                    data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V9'\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639a1831-b75c-4d88-8d82-74b7cc327365",
   "metadata": {},
   "source": [
    "Scatter plot showing correlation between ranks and titer as that is what is actually measured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f55debcf-34b7-4ec9-a695-8f6fb6cf3136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='5-HIAA', y='median_mmNorm', hue='VisitCode',\n",
    "                    data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V9'\").rank())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589cb497-ea1b-4665-8ac8-aec2dbeacb9a",
   "metadata": {},
   "source": [
    "Scatterplot of 5-HIAA un-normalized vs median titer with points colored by visit code. V5 is 2 months and V9 is one year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "85030b23-c193-4225-95f0-cfd0edf5dfb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='5-HIAA', y='median_mmNorm', hue='VisitCode',\n",
    "                    data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cab0143b-d953-4817-8555-5e087df7da13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(np.log10(metab_data.loc[meta_matched.query(\"VisitCode == 'V9'\").index, '5-HIAA']), meta_matched.query(\"VisitCode == 'V9'\")['median_mmNorm'])\n",
    "_ = plt.xlabel('Log 5-HIAA abundance')\n",
    "_ = plt.ylabel('median_mmNorm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8519510e-0f09-4d44-83d7-3ed498742e86",
   "metadata": {},
   "source": [
    "## Correlation with relative abundance data\n",
    "\n",
    "Try normalizing metabolomics to relative abundance before doing correlations. Want to see if this will reduce number of correlated metabolites at 1 year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "22366cd0-afb9-49d0-8ecc-1f9af6dcc0e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.618018</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.006561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>0.445817</td>\n",
       "      <td>0.006429</td>\n",
       "      <td>0.356798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>0.377606</td>\n",
       "      <td>0.023179</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.374775</td>\n",
       "      <td>0.024317</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.371686</td>\n",
       "      <td>0.025611</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>-0.367568</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.355212</td>\n",
       "      <td>0.033508</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Methyl-2-oxovaleric acid</th>\n",
       "      <td>0.335393</td>\n",
       "      <td>0.045535</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>-0.325611</td>\n",
       "      <td>0.052636</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deoxycytidine 5-triphosphate</th>\n",
       "      <td>0.316345</td>\n",
       "      <td>0.060152</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cis-Aconitic acid</th>\n",
       "      <td>0.315573</td>\n",
       "      <td>0.060815</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trans-Aconitic acid</th>\n",
       "      <td>0.315573</td>\n",
       "      <td>0.060815</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.311712</td>\n",
       "      <td>0.064216</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Pyridoxic acid</th>\n",
       "      <td>0.308880</td>\n",
       "      <td>0.066804</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Mannose</th>\n",
       "      <td>0.305792</td>\n",
       "      <td>0.069721</td>\n",
       "      <td>0.483688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "Phenylpyruvic acid           -0.618018  0.000059  0.006561\n",
       "Thymine                       0.445817  0.006429  0.356798\n",
       "myo-Inositol                  0.377606  0.023179  0.483688\n",
       "Adenosine                    -0.374775  0.024317  0.483688\n",
       "Inosine                      -0.371686  0.025611  0.483688\n",
       "5-HIAA                       -0.367568  0.027425  0.483688\n",
       "Pyruvic acid                 -0.355212  0.033508  0.483688\n",
       "4-Methyl-2-oxovaleric acid    0.335393  0.045535  0.483688\n",
       "2-Deoxyuridine               -0.325611  0.052636  0.483688\n",
       "Deoxycytidine 5-triphosphate  0.316345  0.060152  0.483688\n",
       "cis-Aconitic acid             0.315573  0.060815  0.483688\n",
       "trans-Aconitic acid           0.315573  0.060815  0.483688\n",
       "L-Serine                     -0.311712  0.064216  0.483688\n",
       "4-Pyridoxic acid              0.308880  0.066804  0.483688\n",
       "D-Mannose                     0.305792  0.069721  0.483688"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_rel_v5 = metab_data_rel.loc[meta_v5.index]\n",
    "# metab_data_v5 = metab_data_v5[(metab_data_v5 > 0).sum(axis=0) > metab_data_v5.shape[0]*.2]\n",
    "v5_correlations_rel = metab_data_rel_v5.apply(spearmanr, b=meta_v5['median_mmNorm']).transpose()\n",
    "v5_correlations_rel.columns = ['rho', 'p_value']\n",
    "v5_correlations_rel['p_adj'] = p_adjust(v5_correlations_rel['p_value'])\n",
    "v5_correlations_rel = v5_correlations_rel.sort_values('p_value')\n",
    "v5_correlations_rel.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5d511d-53b8-4d44-a6f4-311d5a5b74e1",
   "metadata": {},
   "source": [
    "Phenylpyruvic acide remains very significant at month 2 with no other significant results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d25f672d-e058-4256-bd1d-7cca71ec0534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D-Mannose</th>\n",
       "      <td>-0.559561</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Sorbose</th>\n",
       "      <td>-0.559561</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.525923</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.002304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.501291</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.004257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.488570</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.005285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.473330</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.006116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.469702</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.006116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.468122</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.006116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>-0.466159</td>\n",
       "      <td>0.000496</td>\n",
       "      <td>0.006116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Isoleucine</th>\n",
       "      <td>0.455700</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.007567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>0.450833</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.007567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.449937</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.007567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.430898</td>\n",
       "      <td>0.001428</td>\n",
       "      <td>0.012193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.424879</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.012715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthine</th>\n",
       "      <td>0.424324</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>0.012715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mevalonic acid</th>\n",
       "      <td>0.410066</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.016952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riboflavin</th>\n",
       "      <td>0.409169</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.016952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Kynurenine</th>\n",
       "      <td>0.398583</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.021126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Xylose</th>\n",
       "      <td>-0.385563</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>0.027813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <td>0.383001</td>\n",
       "      <td>0.005072</td>\n",
       "      <td>0.028147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>0.371177</td>\n",
       "      <td>0.006748</td>\n",
       "      <td>0.035666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxycytidine 5-diphosphate</th>\n",
       "      <td>0.366652</td>\n",
       "      <td>0.007506</td>\n",
       "      <td>0.037626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthosine</th>\n",
       "      <td>0.364517</td>\n",
       "      <td>0.007889</td>\n",
       "      <td>0.037626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-pantothenic acid</th>\n",
       "      <td>0.363194</td>\n",
       "      <td>0.008135</td>\n",
       "      <td>0.037626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoxyl sulfate-13C6</th>\n",
       "      <td>-0.359523</td>\n",
       "      <td>0.008853</td>\n",
       "      <td>0.037767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Methionine</th>\n",
       "      <td>0.359139</td>\n",
       "      <td>0.008931</td>\n",
       "      <td>0.037767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melibiose</th>\n",
       "      <td>-0.357901</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.037767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>0.350473</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>0.042883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Arabinose</th>\n",
       "      <td>-0.349064</td>\n",
       "      <td>0.011204</td>\n",
       "      <td>0.042883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Citrulline</th>\n",
       "      <td>0.343515</td>\n",
       "      <td>0.012657</td>\n",
       "      <td>0.045403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    rho   p_value     p_adj\n",
       "D-Mannose                     -0.559561  0.000016  0.000896\n",
       "L-Sorbose                     -0.559561  0.000016  0.000896\n",
       "Guanosine                     -0.525923  0.000062  0.002304\n",
       "Adenosine                     -0.501291  0.000153  0.004257\n",
       "serotonin                      0.488570  0.000238  0.005285\n",
       "5-HIAA                         0.473330  0.000394  0.006116\n",
       "L-tryptophan-D5               -0.469702  0.000443  0.006116\n",
       "L-Serine                       0.468122  0.000466  0.006116\n",
       "myo-Inositol                  -0.466159  0.000496  0.006116\n",
       "L-Isoleucine                   0.455700  0.000687  0.007567\n",
       "L-Proline                      0.450833  0.000796  0.007567\n",
       "Inosine                       -0.449937  0.000818  0.007567\n",
       "L-Leucine                      0.430898  0.001428  0.012193\n",
       "L-Phenylalanine                0.424879  0.001692  0.012715\n",
       "Xanthine                       0.424324  0.001718  0.012715\n",
       "Mevalonic acid                 0.410066  0.002535  0.016952\n",
       "Riboflavin                     0.409169  0.002596  0.016952\n",
       "L-Kynurenine                   0.398583  0.003426  0.021126\n",
       "D-Xylose                      -0.385563  0.004761  0.027813\n",
       "Phenaceturic acid              0.383001  0.005072  0.028147\n",
       "Orotic acid                    0.371177  0.006748  0.035666\n",
       "2-Deoxycytidine 5-diphosphate  0.366652  0.007506  0.037626\n",
       "Xanthosine                     0.364517  0.007889  0.037626\n",
       "D-pantothenic acid             0.363194  0.008135  0.037626\n",
       "indoxyl sulfate-13C6          -0.359523  0.008853  0.037767\n",
       "L-Methionine                   0.359139  0.008931  0.037767\n",
       "Melibiose                     -0.357901  0.009186  0.037767\n",
       "hydroxyphenyllactate           0.350473  0.010859  0.042883\n",
       "L-Arabinose                   -0.349064  0.011204  0.042883\n",
       "L-Citrulline                   0.343515  0.012657  0.045403"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_rel_v9 = metab_data_rel.loc[meta_v9.index]\n",
    "# metab_data_v9 = metab_data_v9[(metab_data_v9 > 0).sum(axis=0) > metab_data_v9.shape[0]*.2]\n",
    "v9_correlations_rel = metab_data_rel_v9.apply(spearmanr, b=meta_v9['median_mmNorm']).transpose()\n",
    "v9_correlations_rel.columns = ['rho', 'p_value']\n",
    "v9_correlations_rel = v9_correlations_rel.loc[~pd.isna(v9_correlations_rel['p_value'])]\n",
    "v9_correlations_rel['p_adj'] = p_adjust(v9_correlations_rel['p_value'])\n",
    "v9_correlations_rel = v9_correlations_rel.sort_values('p_value')\n",
    "v9_correlations_rel.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0195daf9-2633-443f-8165-36a232835ae4",
   "metadata": {},
   "source": [
    "30 things significant at 12 months with relative abundance normalization. Down from 45 (out of 52)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6441be6e-ee30-43d2-9170-e1711910d75d",
   "metadata": {},
   "source": [
    "## Correlation with CLR'd data\n",
    "\n",
    "Now let's try with CLR adjusted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "53bb7f55-5bff-40af-986d-6a855a689d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.637838</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.003173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>0.474646</td>\n",
       "      <td>0.003446</td>\n",
       "      <td>0.191234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatine</th>\n",
       "      <td>0.411840</td>\n",
       "      <td>0.012571</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Pyridoxic acid</th>\n",
       "      <td>0.397426</td>\n",
       "      <td>0.016386</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.391763</td>\n",
       "      <td>0.018130</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>-0.378893</td>\n",
       "      <td>0.022677</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <td>0.366795</td>\n",
       "      <td>0.027776</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.363192</td>\n",
       "      <td>0.029466</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>0.360103</td>\n",
       "      <td>0.030980</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>0.340026</td>\n",
       "      <td>0.042452</td>\n",
       "      <td>0.471219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.317632</td>\n",
       "      <td>0.059060</td>\n",
       "      <td>0.499680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoxyl sulfate-13C6</th>\n",
       "      <td>0.315573</td>\n",
       "      <td>0.060815</td>\n",
       "      <td>0.499680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rho   p_value     p_adj\n",
       "Phenylpyruvic acid      -0.637838  0.000029  0.003173\n",
       "Thymine                  0.474646  0.003446  0.191234\n",
       "Creatine                 0.411840  0.012571  0.382090\n",
       "4-Pyridoxic acid         0.397426  0.016386  0.382090\n",
       "Inosine                 -0.391763  0.018130  0.382090\n",
       "5-HIAA                  -0.378893  0.022677  0.382090\n",
       "Pyridoxal hydrochloride  0.366795  0.027776  0.382090\n",
       "Adenosine               -0.363192  0.029466  0.382090\n",
       "myo-Inositol             0.360103  0.030980  0.382090\n",
       "L-tryptophan-D5          0.340026  0.042452  0.471219\n",
       "Pyruvic acid            -0.317632  0.059060  0.499680\n",
       "indoxyl sulfate-13C6     0.315573  0.060815  0.499680"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_clr_v5 = metab_data_clr.loc[meta_v5.index]\n",
    "# metab_data_v5 = metab_data_v5[(metab_data_v5 > 0).sum(axis=0) > metab_data_v5.shape[0]*.2]\n",
    "v5_correlations_clr = metab_data_clr_v5.apply(spearmanr, b=meta_v5['median_mmNorm']).transpose()\n",
    "v5_correlations_clr.columns = ['rho', 'p_value']\n",
    "v5_correlations_clr['p_adj'] = p_adjust(v5_correlations_clr['p_value'])\n",
    "v5_correlations_clr = v5_correlations_clr.sort_values('p_value')\n",
    "v5_correlations_clr.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b6c1c0-6ae3-41f0-bc16-59ea5fb3f136",
   "metadata": {},
   "source": [
    "Hooray! Still Phenylpyruvic acid and nothing else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d516ce45-39ec-4b55-af84-a8050c0689bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.559604</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.001788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.528185</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.003170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.492113</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.006344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.489765</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.006344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.481313</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.006740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Sorbose</th>\n",
       "      <td>-0.452285</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.012084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Mannose</th>\n",
       "      <td>-0.452285</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.012084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>-0.444558</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.013329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Xylose</th>\n",
       "      <td>-0.440289</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>0.013434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.422360</td>\n",
       "      <td>0.001815</td>\n",
       "      <td>0.020142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.408145</td>\n",
       "      <td>0.002668</td>\n",
       "      <td>0.024818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoxyl sulfate-13C6</th>\n",
       "      <td>-0.407932</td>\n",
       "      <td>0.002683</td>\n",
       "      <td>0.024818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melibiose</th>\n",
       "      <td>-0.401912</td>\n",
       "      <td>0.003143</td>\n",
       "      <td>0.026163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha-D(+)Mannose 1-phosphate</th>\n",
       "      <td>-0.400034</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>0.026163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Arabinose</th>\n",
       "      <td>-0.387612</td>\n",
       "      <td>0.004524</td>\n",
       "      <td>0.033481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylitol</th>\n",
       "      <td>-0.382233</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>0.034389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trans-Aconitic acid</th>\n",
       "      <td>-0.379117</td>\n",
       "      <td>0.005577</td>\n",
       "      <td>0.034389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cis-Aconitic acid</th>\n",
       "      <td>-0.379117</td>\n",
       "      <td>0.005577</td>\n",
       "      <td>0.034389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <td>0.369683</td>\n",
       "      <td>0.006990</td>\n",
       "      <td>0.040839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mevalonic acid</th>\n",
       "      <td>0.364048</td>\n",
       "      <td>0.007976</td>\n",
       "      <td>0.043086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate-d7</th>\n",
       "      <td>-0.363109</td>\n",
       "      <td>0.008151</td>\n",
       "      <td>0.043086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxycytidine 5-diphosphate</th>\n",
       "      <td>0.351198</td>\n",
       "      <td>0.010684</td>\n",
       "      <td>0.053908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    rho   p_value     p_adj\n",
       "Guanosine                     -0.559604  0.000016  0.001788\n",
       "Adenosine                     -0.528185  0.000057  0.003170\n",
       "Inosine                       -0.492113  0.000211  0.006344\n",
       "L-tryptophan-D5               -0.489765  0.000229  0.006344\n",
       "serotonin                      0.481313  0.000304  0.006740\n",
       "L-Sorbose                     -0.452285  0.000762  0.012084\n",
       "D-Mannose                     -0.452285  0.000762  0.012084\n",
       "myo-Inositol                  -0.444558  0.000961  0.013329\n",
       "D-Xylose                      -0.440289  0.001089  0.013434\n",
       "5-HIAA                         0.422360  0.001815  0.020142\n",
       "L-Serine                       0.408145  0.002668  0.024818\n",
       "indoxyl sulfate-13C6          -0.407932  0.002683  0.024818\n",
       "Melibiose                     -0.401912  0.003143  0.026163\n",
       "alpha-D(+)Mannose 1-phosphate -0.400034  0.003300  0.026163\n",
       "L-Arabinose                   -0.387612  0.004524  0.033481\n",
       "Xylitol                       -0.382233  0.005168  0.034389\n",
       "trans-Aconitic acid           -0.379117  0.005577  0.034389\n",
       "cis-Aconitic acid             -0.379117  0.005577  0.034389\n",
       "Phenaceturic acid              0.369683  0.006990  0.040839\n",
       "Mevalonic acid                 0.364048  0.007976  0.043086\n",
       "p-cresol sulfate-d7           -0.363109  0.008151  0.043086\n",
       "2-Deoxycytidine 5-diphosphate  0.351198  0.010684  0.053908"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_clr_v9 = metab_data_clr.loc[meta_v9.index]\n",
    "# metab_data_v9 = metab_data_v9[(metab_data_v9 > 0).sum(axis=0) > metab_data_v9.shape[0]*.2]\n",
    "v9_correlations_clr = metab_data_clr_v9.apply(spearmanr, b=meta_v9['median_mmNorm']).transpose()\n",
    "v9_correlations_clr.columns = ['rho', 'p_value']\n",
    "v9_correlations_clr = v9_correlations_clr.loc[~pd.isna(v9_correlations_clr['p_value'])]\n",
    "v9_correlations_clr['p_adj'] = p_adjust(v9_correlations_clr['p_value'])\n",
    "v9_correlations_clr = v9_correlations_clr.sort_values('p_value')\n",
    "v9_correlations_clr.head(22)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b30b3e-9c54-41db-a374-34c7291db186",
   "metadata": {},
   "source": [
    "Down to 21 significant things at 1 year with CLR so more \"advanced\" normalizations keep getting rid of significant results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0c0a95-4107-4ed7-a88d-bcab473c7b4e",
   "metadata": {},
   "source": [
    "## Compare normalizations\n",
    "\n",
    "To better understand what the normalization are shifting we compare between normalizations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9fb334e-e935-4730-b3af-2f7c2922a1c4",
   "metadata": {},
   "source": [
    "### V5\n",
    "\n",
    "These are scatterplots of p-values between normalization at 2 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9289c443-8c31-4820-a89e-d22499464b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.9127257622829813, pvalue=3.543830233449991e-44)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_rel.loc[v5_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 Relative abundanace p-values')\n",
    "pearsonr(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_rel.loc[v5_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e50d939-bf00-41dc-8481-35c6ad71af89",
   "metadata": {},
   "source": [
    "Un-normalized and relative abundance are highly correlated but a bit of variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6e7c1ab8-fa75-455b-9072-22e394563882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.7195196445960201, pvalue=5.7807995753662e-19)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2499820f-6d45-4767-9a33-e79a3c178950",
   "metadata": {},
   "source": [
    "Much more spread with un-normalized vs CLR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82146a1f-0656-41c7-85cb-dd963d54ae5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.8533849875294544, pvalue=1.2897044107726376e-32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v5_correlations_rel['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations_rel.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Relative abundanace p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v5_correlations_rel['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations_rel.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aabee09e-4219-4377-8c10-ba96d8f42f75",
   "metadata": {},
   "source": [
    "Less correlation between relative abundance and CLR but still pretty good.\n",
    "\n",
    "Across all normalizations phenylpyruvic acid is super significant. You can definitely see that relative abundance and CLR shift the p-value distributions but they are much more strongly correlated with each other than the raw data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42464bf1-6ac3-4b3c-b018-ebe8a09ba7c1",
   "metadata": {},
   "source": [
    "### V9\n",
    "\n",
    "Looking at the 1 year results.\n",
    "\n",
    "To do:\n",
    "* Try plotting L-tryptophan-D5, it is a standard, potentially use as a normalization factor (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a41bc7d4-7f61-4769-84e3-55e7a0331e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'5-HIAA',\n",
       " 'Adenosine',\n",
       " 'Guanosine',\n",
       " 'Inosine',\n",
       " 'L-Serine',\n",
       " 'L-tryptophan-D5',\n",
       " 'Mevalonic acid',\n",
       " 'Phenaceturic acid',\n",
       " 'serotonin'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(v9_correlations.query('p_adj < .05').index) & set(v9_correlations_rel.query('p_adj < .05').index) & set(v9_correlations_clr.query('p_adj < .05').index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed0ddd3-5a12-490e-a6c6-ca9457712dcd",
   "metadata": {},
   "source": [
    "These are the 10 compounds that are significant in all 3 methods. The L-tryptophan-D5 is a spike-in. There are multiple spike-ins used and these is the only one that comes us as significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "758c61c7-0f88-4e0c-b2c3-1c80008f4959",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shaffmic/miniconda3/envs/imc/lib/python3.9/site-packages/matplotlib_venn/_venn3.py:117: UserWarning: Bad circle positioning\n",
      "  warnings.warn(\"Bad circle positioning\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = venn3([set(v9_correlations.query('p_adj < .05').index), set(v9_correlations_rel.query('p_adj < .05').index), set(v9_correlations_clr.query('p_adj < .05').index)], set_labels=('un-normalized', 'relative abundance', 'CLR'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1226f73a-dadb-4777-9c08-71ae69dcbba7",
   "metadata": {},
   "source": [
    "The venn diagram shows the 10 that are shared by all. 20 are only in the normalized data. Relative abundance doesn't add anything unique and CLR adds two unique results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2b7a0355-11d7-479f-b19d-cbbcb6772383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.5385890195125794, pvalue=1.0785552863603918e-09)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_rel.loc[v9_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 Relative abundanace p-values')\n",
    "pearsonr(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_rel.loc[v9_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72d019d-d105-4ac3-a590-624fb9072398",
   "metadata": {},
   "source": [
    "Much worse correlation between normalization strategies at one year time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3aff190e-1a00-40ce-97f4-3f1f6e7f6da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.13878960742409188, pvalue=0.1462994527423617)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62d699f-1967-469e-86c7-27a860056939",
   "metadata": {},
   "source": [
    "Much worse correlation between normalization strategies at one year time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8682195e-7b4e-4d05-9757-89a1a1ce8445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.7595074551548853, pvalue=4.39265816060488e-22)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v9_correlations_rel['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations_rel.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 relative abundance p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v9_correlations_rel['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations_rel.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554c961c-aae2-4f49-93a7-a56b6bc165d1",
   "metadata": {},
   "source": [
    "Much worse correlation between normalization strategies at one year time point."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3906404d-c3ac-4003-9d69-1b24c07205e9",
   "metadata": {},
   "source": [
    "## Plot tryptophan vs titers\n",
    "\n",
    "This is the spike-in which was found to be significant at year 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "21647c37-a082-453c-8973-c4c81036cb93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(metab_data_v9.loc[meta_v9.index, 'L-tryptophan-D5'], meta_v9['median_mmNorm'])\n",
    "_ = plt.xlabel('L-tryptophan-D5 abundance')\n",
    "_ = plt.ylabel('Median normalized titer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1ff4794-5b7f-4902-bcf5-1ad34e41f01e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ub7D6/M2pr70xvw/BgL4MDvRj8KAvgwd9GTzoS/9W3yVDpgZZb7zxhgoKCvTtt9+qVatWZhZdrUOHDkmSzjjjjGrPd+jQodJ1dSkpKdG9996rsLAw/e1vf5PVajWnoS4qKipSenp6g9bprzIyMnzdBJiEvgwO9GPwoC+DB30ZPOjL4GNqkHX06FF17969QQIsSc4U7REREdWedxx3NZX7X//6V2VmZur5559XdHS0OY0EAAAA0KiYGmR17dpV+/bt04kTJ9SsWTMzi66W1WpVRUWFLBZLrde5Mtz3zTff6L333tOoUaOUnJxsUgvdExkZWePUx8bAbrc7v8lJSEho8JFEmIe+DA70Y/CgL4MHfRk86Ev/tn37dhUVFXl8v6lB1pNPPqmbb75ZN998s+655x717dtXTZo0MbOKSpo1a6ajR4/W+AYUFxdLOhm81Obw4cN65JFHdOaZZ+qpp54yvZ2uslgs/ID9H6vVynsRJOjL4EA/Bg/6MnjQl8GDvvQ/dQ3i1MXUIOv9999XTEyM0tPTNXHiRIWEhKhFixY1TuezWCxauXKlx/V16NBBR48eVW5urrp06VLlvGMtlmNtVk3mzJmjI0eOqGfPnpo+fXqlc5s3b5YkLVu2TNnZ2YqLi9Ott97qcZsBAAAABDdTg6wlS5ZU+n+73a6jR4/WeH19I8T4+HhlZmZq586d1e5ntXPnTud1tXGs2dq6dau2bt1a7TWZmZnKzMzUwIEDCbIAAAAA1MjUIOvZZ581s7g6DRkyRJ999pmWLl2qcePGVTqXn5+vtLQ0hYWFafDgwbWW89xzz9WYqn3GjBl69913NWXKFN1xxx2mtR0AYK78gmItS8vWpqw8FZWUKzI8VAlx7TQiMUZRLaqfUQEAgDeYGmRdddVVZhZXpxEjRqhTp05avXq13n//fY0fP17SybVYU6dOVWFhoa699lq1a9fOeU9ZWZlycnIkSTExMQoLC2vQNgMAzFVSZte8xRlKXZejcnvlREfpmblasGybhg2I0aTkBDUJY80DAMD7TA2yGlpERISee+45TZo0SdOnT9eiRYsUHR2t9evX69ChQ+rVq5fuv//+SvccPHhQo0aNkiSlpqaSqh0AAlhJmV3T5q3Rpqy8Gq8ptxtaujZbv+Qe17SUJIUTaAEAvCzEG4WWlpbqww8/1OTJkzVixAgNGjRIknTkyBE98sgjzrVSZkhMTNTChQs1cuRI/frrr/rmm2/UokUL3XbbbZo/f76aN29uWl0AAP8yb3FGrQHWqTZl5WneYjb8BAB4n+kjWbt379att96q7Oxs5/5UjgQXv/76qz755BN98cUXeuGFFzR8+HBT6rTZbJo1a5ZL10ZHR2v79u0ulz116lRNnTrV06YBALwk/1ixUtfluHVP6rocjb+sB2u0AABeZepIVkFBgW666Sbt2bNHZ555pm688UbFxMQ4z7do0UJdu3ZVSUmJ7r77bmVmZppZPQCgEVn2Q3aVNVh1KbcbWp7mXmAGAIC7TA2y/vnPf+rXX3/VxRdfrC+//FIPPfRQpaQTsbGx+uyzzzR8+HCVl5fr7bffNrN6AEAj4uo0wdNlZB02uSUAAFRmapC1bNkyhYaGasaMGQoPD6/2GqvVqunTp6tJkyZKS0szs3oAQCNSVFLeoPcBAOAqU4Osffv2qXv37mrbtm2t17Vp00Znn322cnNzzaweANCIRIZ7tqzY0/sAAHCVqUGWxWJRUVGRS9dWVFSoSZMmZlYPAGhEesfV/oVeTRLi2tV9EQAA9WBqkBUbG6t9+/bVOUK1f/9+ZWVlKTY21szqAQCNyKUDYxVqtbh1T6jVohGJMXVfCABAPZgaZDkSWkyfPt2Zvv10paWlmjp1qgzD0NChQ82sHgDQiES1jNCwAe4FTMMGxJC+HQDgdaZOTJ84caIWLVqkr7/+WmPHjtWoUaOUl3cy+9O3336rHTt2aOHChcrOzlb79u01YcIEM6sHADQyKckJ+iX3uEuZBnvHtdWk5IQGaBUAoLEzNchq3ry55s2bp1tvvVWbNm3S5s2bneduueUWSZJhGGrfvr1ee+01tWrVyszqAQCNTHiYVdNSkjRvcYZS1+VUu29WqNWiYQNiNCk5QU3CrD5oJQCgsTE9xVJcXJz+85//6OOPP9bXX3+tHTt26Pjx44qMjNTZZ5+tSy65ROPHj1fLli3NrhoA0AiFh1k1ZWxfjb+sh5an5Sgj67CKSsoVGR6qhLh2GpHIFEEAQMPySh7byMhITZw4URMnTvRG8QAAVBHVIkLjhts0brjN100BADRypia+eOSRR/SPf/zDpWunT5/OmiwAAAAAQcfUIOuTTz7Rt99+69K1P/74ozZu3Ghm9QAAAADgcx5PF9yzZ4/+85//VDm+f/9+vfzyyzXeZxiGfv31V2VmZioqKsrT6gEAAADAL3kcZHXp0kVfffWV9uzZ4zxmsVi0f/9+vf7667Xe69hDa8SIEZ5WDwAAAAB+yeMgy2q1aurUqZo7d67z2Lp169SiRQv16NGjxvtCQkLUtGlT9erVSykpKZ5WDwAAAAB+qV7ZBS+88EJdeOGFzv/v0aOHbDab5s+fX++GAQAAAEAgMjWF+7PPPqu2bduaWSQAAAAABBRTg6yrrrrKzOKAWuUXFGtZWrY2ZeWx8SgAAAD8hsdB1r/+9S9J0mWXXabmzZtXOuaOMWPGeNoENFIlZXbNW5yh1HU5Krcblc6lZ+ZqwbJtGjYgRpOSE9QkzOqjVgIAAKCx8jjIeuyxx2SxWHT++ec7gyzHMXcQZMEdJWV2TZu3Rpuy8mq8ptxuaOnabP2Se1zTUpIUTqAFAACABuRxkNWpU6eTBYSGVjkGeMu8xRm1Blin2pSVp3mLMzRlbF/vNgoAAAA4hcdB1ooVK1w6Bpgl/1ixUtfluHVP6rocjb+sB2u0AAAA0GBCfFXxb7/9pi1btviqegSgZT9kV1mDVZdyu6Hlae4FZgAAAEB9mBpk9ezZU9ddd51L1/75z3/WpEmTzKweQc7VaYKny8g6bHJLAAAAgJqZGmQZhiHDqHukobCwUIcOHdKxY8fMrB5BrqikvEHvAwAAADzh8ZqsnTt3KiUlpUpQlZGRoYsvvrjG+wzD0LFjx1RcXKyzzjrL0+rRCEWGe/Zx9fQ+AAAAwBMeP31269ZN/fr105IlSyodLy0t1YEDB+q8PyQkRLfeequn1aMR6h3XVumZuW7flxDXzgutAQAAAKpXr6/4H374YV144YWSTo5QPfroozrrrLM0efLkGu+xWCxq1qyZ4uPjFRMTU5/q0chcOjBWHy7b7lbyi1CrRSMS+ZwBAACg4dQryGrfvr2uuuoq5/8/+uijatu2baVjgFmiWkZo2IAYLV2b7fI9wwbEkL4dAAAADcrUxSrbtm0zszigipTkBP2Se9ylTIO949pqUnJCA7QKAAAA+B+f7ZMFeCI8zKppKUkaOShWoVZLtdeEWi0aOShWT6UkqUmYtYFbCAAAgMaOtGsIOOFhVk0Z21fjL+uh5Wk5ysg6rKKSckWGhyohrp1GJDJFEAAAAL5DkIWAFdUiQuOG2zRuuM3XTQEAAACcmC4IAAAAACYiyAIAAAAAExFkAQAAAICJCLIAAAAAwEQEWQAAAABgIo+zCz744IP1rtxisej555+vdzkAAAAA4C88DrI+/fRTWSwWGYZR5ZzF8r9NYk8/7zhnGAZBFgAAAICg43GQlZycXCmYcsjMzNTmzZsVGhqqwYMHq0ePHmrVqpWKi4u1c+dOrVy5UsXFxbr44ot17rnn1qvxAAAAAOBvPA6ynnvuuSrHsrOzNXr0aHXv3l1z5sxRly5dqlyTm5ur2267TWvWrNGdd97pafUAAAAA4JdMTXzx8ssvq7i4WK+++mq1AZYktW/fXi+//LLsdrtefvllM6sHAAAAAJ/zeCSrOt9//726deum2NjYWq/r1KmTbDab1q9fb2b18AP5BcValpatTVl5KiopV2R4qBLi2mlEYoyiWkT4unkAAACA15kaZJWWlqqiosKla0+cOFFt0gwEppIyu+YtzlDquhyV2yv3a3pmrhYs26ZhA2I0KTlBTcKsPmolAAAA4H2mTheMjY3Vzp07tW3btlqvW7NmjbKzs9WtWzczq4ePlJTZNW3eGi1dm10lwHIotxtaujZbT85bo5IyewO3EAAAAGg4pgZZycnJMgxDt912m3788cdqr1mxYoXuueceWSwWXX311WZWDx+ZtzhDm7LyXLp2U1ae5i3O8HKLAAAAAN8xdbrgtddeqy+//FLp6emaMGGCOnfurO7du6tp06Y6ceKEtm3bpoMHD8owDF166aVKTk42s3r4QP6xYqWuy3HrntR1ORp/WQ/WaAEAACAomRpkNWnSRPPmzdPzzz+vf//739q3b5/27dtXucLQUN100026/fbbzawaPrLsh5qnCNak3G5oeVqOxg23ealVAAAAgO+YGmRJUosWLfT000/rrrvu0n//+1/t3r1bx44dU+vWrXX22Wdr6NChatWqldnVwkdcnSZ4uoyswwRZAAAACEqmB1kO7du31x//+EdvFQ8/UVRS3qD3AQAAAP7Oa0FWRUWFNm/erF27dqmgoEDXXXedysrKdODAgRo3KkbgiQz37CPk6X0AAACAv/PKk+6iRYv0yiuv6ODBg85j1113nX799VeNGjVKl19+uZ5++mlFRJD4IND1jmur9Mxct+9LiGvnhdYAAAAAvmdqCndJeuGFF/TYY4/pwIEDslgsslr/t/HsgQMHZLfbtWTJEt18880qL2fKWKC7dGCsQq0Wt+4JtVo0IjHGSy0CAAAAfMvUIGvt2rWaN2+eIiIiNG3aNP3www/q06eP83xiYqL++te/KjIyUj/99JM++ugjM6uHD0S1jNCwAe4FTMMGxJC+HQAAAEHL1CBr/vz5slgseuaZZ3TNNdeoefPmVa658sor9de//lWGYeizzz4zs3r4SEpygnrHtXXp2t5xbTUpOcHLLQIAAAB8x9QgKz09Xe3atdPll19e63XDhw9Xhw4dtHPnTjOrh4+Eh1k1LSVJIwfVPHUw1GrRyEGxeiolSU3CrNVeAwAAAAQDUxNf/Pbbb+rRo4dL155xxhnaunWrmdXDh8LDrJoytq/GX9ZDy9NylJF1WEUl5YoMD1VCXDuNSGSKIAAAABoHU4Os1q1ba+/evXVeZxiG9u3bp6ioKDOrhx+IahGhccNtbDQMAACARsvU6YL9+vXTsWPHtGTJklqv++STT5Sfn6/zzjvPzOoBAAAAwOdMDbImTJggwzA0ffp0paamVjlfUVGhhQsXavr06bJYLLrmmmvMrB4AAAAAfM7U6YIDBgzQzTffrDfeeENTpkxRs2bNVFZWJkkaM2aM9uzZoxMnTsgwDI0bN04XXHCBmdUDAAAAgM+ZGmRJ0v3336/o6Gi98sorysvLcx7ftGmTJKlFixaaNGmSUlJSzK4aAAAAAHzO9CBLkq655hr98Y9/1Pr167Vjxw4VFBQoMjJSZ599tgYMGKDIyEhvVAsAAAAAPueVIEuSwsLCNHDgQA0cOLDa83l5eSopKVGnTp281QQAAAAAaHCmJr64/vrr9cgjj6iwsLDOa++44w4NHz7czOoBAAAAwOdMHcn64YcfZLFYtHnzZs2dO1dnnnlmrdcbhmFm9QAAAADgc6aOZEknA6fMzEyNHTtWGzZsMLt4AAAAAPBrpgdZvXr10kUXXaTDhw/r+uuv1+eff252FQAAAADgt0wPsiIjI/X666/r6quvVklJiR544AHNmjXL7GoAAAAAwC+ZHmRJUkhIiJ566indf//9kqTXXntN99xzj0pKSpzXWCwWb1QNAAAAAD7llSDL4eabb9aLL76oJk2a6KuvvtJ1112nw4cPSzqZ4h0AAAAAgo1XgyxJuuyyy/TOO+8oKipKGRkZGjt2rHbs2KGIiAhvVw0AAAAADc7rQZYk9e3bVx999JHOOuss7d+/X+PHj9fevXsbomoAAAAAaFANEmRJUpcuXfTRRx+pf//+OnbsmHbt2tVQVQMAAABAgzE1yOrUqZPat29f4/lWrVrp7bff1hVXXMFGxAAAAACCUqiZha1YsaLOa8LCwvT3v/9dI0eO1PHjx82sHgAAAAB8ztQgyx0jRowwrazdu3dr9uzZ+umnn5SXl6eOHTvq8ssv1+TJk9W0aVO3ylqzZo3efPNNbdiwQSUlJTrzzDM1fPhwpaSkqHXr1qa1GQAAAEBw8jjI+te//iXpZPbA5s2bVzrmjjFjxnjaBEnSxo0bNXHiRBUWFqpPnz5KSEjQzz//rNdff10rV67UBx984GxfXRYtWqRHH31U0slkHe3atdOWLVv0xhtv6NNPP9WCBQsUHR1dr/YCAAAACG4eB1mPPfaYLBaLzj//fGcQ4zjmjvoEWeXl5br33ntVWFioGTNmOMsqLi7WPffcoxUrVmjmzJl64okn6ixr7969evLJJxUWFqZ//OMfuuCCCyRJZWVlevLJJ7Vo0SJNnz5d//jHPzxuLwAAAIDg53GQ1alTp5MFhIZWOdZQlixZor179yopKalSsBYREaFnnnlGQ4cO1ccff6y77rpLrVq1qrWszz77TGVlZbrmmmucAZZ0cg3Zfffdp0WLFmn16tUqLS1VkyZNvPaaAAAAAAQ2j4Os6pJcuJL4wkyO+qpb3xUVFaXExEStXLlSq1at0hVXXFFrWbfccouuuOKKatdw2e12SVJISIhCQhos6z0AAACAAOSzxBdmyMzMlCTFx8dXe75bt25auXKltm3bVmeQFRISopiYmCrH8/PzNW3aNEnSH/7wh0ojd2YzDMMZ0DVGp772xvw+BAP6MjjQj8GDvgwe9GXwoC/9W323mwroIOvQoUOSpDPOOKPa8x06dKh0nTv+8Y9/aPXq1UpPT1dpaamSk5NdWttVH0VFRUpPT/dqHYEiIyPD102ASejL4EA/Bg/6MnjQl8GDvgw+HgdZ48ePr3flFotF7733nsf3FxYWSjq5Bqs6juOO69zxxRdfaOvWrZIkq9WqY8eOad++fYqLi/OwtQAAAAAaA4+DrJ9++qnelbubifB0VqtVFRUVdZbjyXDf66+/rqioKGVnZ2vOnDn68ssv9fPPP2vx4sU688wzPW1yrSIjI2uc+tgY2O125zc5CQkJslqtPm4RPEVfBgf6MXjQl8GDvgwe9KV/2759u4qKijy+3+Mga8qUKR5XapZmzZrp6NGjNb4BxcXFkk4GL+7q2LGjJMlms+mll15SQUGBVq9erbfeektTp071vNG1sFgs/ID9H6vVynsRJOjL4EA/Bg/6MnjQl8GDvvQ/9R0MCuggq0OHDjp69Khyc3PVpUuXKucda7Eca7Pq48orr9Tq1au1ZcuWepcFAAAAIHgFdD5yx9S6nTt3VnvecdyVKXjvvvuu7rvvPm3atKna8469scrLyz1pKgAAAIBGwmvZBfPy8lRUVFRlPVR5ebmKi4t14MABrVy5UtOnT/e4jiFDhuizzz7T0qVLNW7cuErn8vPzlZaWprCwMA0ePLjOstLT07VkyRK1atVKvXv3rnL+22+/lXRyziwAAAAA1MT0IGvhwoV6+eWXlZeX59L19QmyRowYoU6dOmn16tV6//33nRkPi4uLNXXqVBUWFuraa69Vu3btnPeUlZUpJydHkhQTE6OwsDBJ0p/+9CctWbJEH374oS688EINHTpU0smkGe+9954++eQThYeH6/rrr/e4vQAAAACCn6lB1po1a/T444+7dG1UVJSGDBlSr/oiIiL03HPPadKkSZo+fboWLVqk6OhorV+/XocOHVKvXr10//33V7rn4MGDGjVqlCQpNTVV0dHRkqT+/fvrnnvu0Ysvvqhbb71VvXv3VseOHZWZmamcnBxFRkbqhRdeqHbDYgAAAABwMHVN1oIFCyRJAwcO1Hvvvad//etfkqTk5GQtXbpU77zzjv7f//t/kqQzzzxTM2bMqHediYmJWrhwoUaOHKlff/1V33zzjVq0aKHbbrtN8+fPV/PmzV0u65ZbbtHbb7+tiy66SHv37tW3336r8vJyjRkzRosXL9awYcPq3V4AAAAAwc3Ukaz09HSFhobqb3/7m8444wxJUmxsrDIyMhQbG6vY2FglJiaqRYsW+uijj7Rw4UJdc8019a7XZrNp1qxZLl0bHR2t7du313j+ggsu0AUXXFDvNgEAAABonEwdycrPz1fnzp2dAZZ0MrPf7t27K+1ldeedd8pqterzzz83s3oAAAAA8DlTg6zQ0FC1aNGi0rGYmBgZhqFdu3Y5j7Vp00axsbHKysoys3oAAAAA8DlTg6x27dpp//79lY45NgnesWNHpeNNmjRRQUGBmdUDAAAAgM+ZGmT17dtXR44c0eLFi53H4uLiZBiGVq1a5Tx27Ngx7dmzR23btjWzegAAAADwOVODrHHjxskwDE2dOlV33323SktL1bdvX3Xo0EFffPGFXn31Va1cuVJ33HGHiouL1a1bNzOrBwAAAACfMzXIGjBggFJSUmS327Vy5Uo1adJEoaGh+vOf/yzDMDR79mzddtttSktLkySlpKSYWT0AAAAA+JypKdwl6b777tOFF16o1atXO4/dcMMNOn78uN566y0VFhaqVatWuvvuuzVo0CCzqwcAAAAAnzI9yJJObhCcmJhY6diUKVN0yy23KD8/X23atJHVavVG1QAAAADgU14JsmqsLDRU7du3b8gqAQAAAKBBeSXI+vXXX7VlyxYdP368zmuTk5O90QQAAAAA8AlTg6zS0lI98sgj+uKLL1y63mKxEGQBAAAACCqmBllz5szRkiVLJElhYWGKiopSaGiDzkgEAAAAAJ8yNQJasmSJLBaLbr/9dk2ePFlhYWFmFg8AAAAAfs/UIOvAgQM688wzNWXKFDOLBQAAAICAYepmxK1atVKzZs3MLBIAAAAAAoqpI1kXXnihlixZory8PLVt29bMouEH8guKtSwtW5uy8lRUUq7I8FAlxLXTiMQYRbWI8HXzAAAAAL9gapB1xx13aPny5XrwwQf18ssvq3nz5mYWDx8pKbNr3uIMpa7LUbndqHQuPTNXC5Zt07ABMZqUnKAmYWwyDQAAgMbN1CCrc+fOmjt3rq6//npdfPHF6tevn9q0aSOLxVLt9RaLRc8884yZTYDJSsrsmjZvjTZl5dV4Tbnd0NK12fol97impSQpnEALAAAAjZipQdahQ4f06KOPyjAMHT9+XP/973+rvc5iscgwDIKsADBvcUatAdapNmXlad7iDE0Z29e7jQIAAAD8mKlB1gsvvKCcnBxZLBb17dtXnTt3VpMmTcysAg0o/1ixUtfluHVP6rocjb+sB2u0AAAA0GiZGmR99913Cg0N1RtvvKFBgwaZWTR8YNkP2VXWYNWl3G5oeVqOxg23ealVAAAAgH8zNYX7sWPHdPbZZxNgBQlXpwmeLiPrsMktAQAAAAKHqUFWdHS0iouLzSwSPlRUUt6g9wEAAADBwNQg68orr9S+ffu0evVqM4uFj0SGezab1NP7AAAAgGBgapB10003qW/fvrr77rv15ptvaufOnTp+/LgqKipq/AP/1TvOsw2lE+LamdwSAAAAIHCYOuRwww03qLS0VMePH9ff//53/f3vf6/1eovFoi1btpjZBJjo0oGx+nDZdreSX4RaLRqRGOPFVgEAAAD+zdSRrJ9++kmbN2+WJBmG4dIf+K+olhEaNsC9gGnYgBjStwMAAKBRM3Uk69133zWzOPiBlOQE/ZJ73KVMg73j2mpSckIDtAoAAADwX6YGWRaLRT179lTz5s3NLBY+FB5m1bSUJM1bnKHUdTnVTh0MtVo0bECMJiUnqEmY1QetBAAAAPyHqUHWY489psOHD+vrr79WVFSUmUXDh8LDrJoytq/GX9ZDy9NylJF1WEUl5YoMD1VCXDuNSGSKIAAAAOBgapC1f/9+nXXWWQRYQSqqRYTGDbdp3HCbr5sCAAAA+C1TE1+0adNGBQUFJLQAAAAA0GiZGmTdeuut2r9/v5555hkVFxebWTQAAAAABARTpwtKUr9+/fTee+9p4cKF6tmzp9q3b6+IiOrX61gsFj3//PNmNwEAAAAAfMbUIOvJJ5+UxWKRYRgqLi7W+vXrq73OcQ1BFgAAAIBgY2qQlZycLIvFYmaRAAAAABBQTA2ynnvuOTOLAwAAAICAY2riCwAAAABo7ExPfCFJFRUVWrJkib7++mvt3r1bJ06cULNmzXTWWWfpd7/7nZKTkxUWFuaNquGB/IJiLUvL1qasvEa7yTDvAQAAAMxiepB18OBB3XbbbdqyZUuV/bIyMzO1fPlyLViwQK+88oo6d+5sdvVwQ0mZXfMWZyh1XY7K7ZX7Kj0zVwuWbdOwATGalJygJmFWH7XSu3gPAAAAYDZTg6ySkhKlpKQoMzNTERERGj58uHr37q1mzZrp2LFj2rRpk1asWKEtW7bo9ttv18cff6wmTZqY2QS4qKTMrmnz1mhTVl6N15TbDS1dm61fco9rWkqSwoMsyOA9AAAAgDeYGmQtWLBAmZmZOvvsszVv3jxFR0dXuWbv3r1KSUnR9u3btWjRIl177bVmNgEumrc4o9bg4lSbsvI0b3GGpozt691GNTDeAwAAAHiDqYkvvvzyS1ksFr344ovVBliS1KVLF7300ksyDEOff/65mdXDRfnHipW6Lsete1LX5Si/oNhLLWp4vAcAAADwFlODrKysLJ111lnq0aNHrdf16NFDZ599tnbt2mVm9XDRsh+yq6w/qku53dDyNPeCEn/GewAAAABvMTXIKikpUbNmzVy6tmnTpiosLDSzerjI1Slyp8vIOmxyS3yH9wAAAADeYmqQdeaZZyorK0snTpyo9boTJ04oKytLHTp0MLN6uKiopLxB7/NHvAcAAADwFlODrKSkJBUXF+uvf/1rrdc9//zzKikpUVJSkpnVw0WR4Z7lO/H0Pn/EewAAAABvMTXIuvHGGxUWFqaPP/5YKSkpWrVqlY4cOaKysjIdOXJEq1at0s0336yFCxcqLCxMN954o5nVw0W949p6dF9CXDuTW+I7vAcAAADwFlO/lj/rrLP09NNP69FHH9Xq1au1evXqKtcYhqHQ0FD95S9/0dlnn21m9XDRpQNj9eGy7W4lfgi1WjQiMcaLrWpYvAcAAADwFlNHsiTpyiuv1Pvvv68LLrhAoaGhMgzD+cdqtWrw4MF677339Ic//MHsquGiqJYRGjbAvWBh2IAYRbWI8FKLGh7vAQAAALzFKwtMzj33XL355psqKirS3r17dfz4cTVr1kwxMTGKjIz0RpVwU0pygn7JPe5Slr3ecW01KTmhAVrVsHgPAAAA4A2mj2SdKjIyUjabTf369VN8fDwBlh8JD7NqWkqSRg6KVajVUu01oVaLRg6K1VMpSWoSZm3gFnof7wEAAAC8wWup0oqKilRQUCC73S7DqHndS6dOnbzVBNQhPMyqKWP7avxlPbQ8LUcZWYdVVFKuyPBQJcS104jE4J8ex3sAAAAAs5keZH377bd66aWXtG3btjqvtVgs2rJli9lNgJuiWkRo3HCbxg23+bopPsN7AAAAALOYGmT9+OOPuu2221RRUVHr6JWDK9cAAAAAQCAxNch64403ZLfbFR8frylTpqhr166KiGCqFQAAAIDGw9Qga/369QoPD9ebb76pdu3YtBUAAABA42NqdsGioiLFxcURYAEAAABotEwNsjp16qS8vLr3HAIAAACAYGVqkHXZZZfp0KFDWrNmjZnFAgAAAEDAMDXImjx5srp166YHH3xQX3/9tUpLS80sHgAAAAD8nqmJLx577DF17NhRO3bs0B133CGr1apWrVopLCys2ustFotWrlxpZhMAAAAAwKdMDbKWLFni/LthGCovL691jZbFYjGzegAAAADwOVODrGeffdbM4gAAAAAg4JgaZF111VVmFgcAAAAAAcfUxBcAAAAA0NgRZAEAAACAiQiyAAAAAMBEBFkAAAAAYCJTE18gOOQXFGtZWrY2ZeWpqKRckeGhSohrpxGJMYpqEeHr5gEAAAB+jSALTiVlds1bnKHUdTkqtxuVzqVn5mrBsm0aNiBGk5IT1CTM6qNWAgAAAP6NIAuSTgZY0+at0aasmjePLrcbWro2W7/kHte0lCSFE2gBAAAAVbAmC5KkeYszag2wTrUpK0/zFmd4uUUAAABAYPLKSFZFRYV2796tY8eOyW63yzCMGq8dMGCAN5oAN+QfK1bquhy37kldl6Pxl/VgjRYAAABwGtODrI8++kgvvfSSjh49Wue1FotFW7ZsMbsJcNOyH7KrrMGqS7nd0PK0HI0bbvNSqwAAAIDAZOp0wdTUVD355JPKz8+XYRh1/qmoqDCzenjI1WmCp8vIOmxySwAAAIDAZ+pI1vz58yVJSUlJeuCBBxQXF6fw8HAzq4AXFJWUN+h9AAAAQDAzNcjavHmzmjZtqldeeUXNmzc3s+ha7d69W7Nnz9ZPP/2kvLw8dezYUZdffrkmT56spk2bulXWN998o/fee0+bNm1SQUGBWrdurX79+unmm2/Wueee66VX4FuR4Z59DDy9DwAAAAhmpj4ll5WVKS4urkEDrI0bN2rixIkqLCxUnz59lJCQoJ9//lmvv/66Vq5cqQ8++MDl9sycOVNz586VxWLROeeco44dO2rXrl1atmyZUlNTNWPGDF111VVefkUNr3dcW6Vn5rp9X0JcOy+0JnCwaTMAAACqY2qQFRsbq/3795tZZK3Ky8t17733qrCwUDNmzNCYMWMkScXFxbrnnnu0YsUKzZw5U0888USdZf3444+aO3euIiMjNXfuXCUmJjrPffjhh3ryySf1xBNPKDExUZ06dfLaa/KFSwfG6sNl291KfhFqtWhEYowXW+W/2LQZAAAAtTE18cUf/vAHHTlyRF988YWZxdZoyZIl2rt3r5KSkpwBliRFRETomWeeUdOmTfXxxx/rt99+q7Osf/3rX5Kkm2++uVKAJUnXXHONhgwZotLSUi1dutTcF+EHolpGaNgA9wKmYQMa52iNY9PmpWtrzsjo2LT5yXlrVFJmb+AWAgAAwNdMDbKuv/56JSUl6fHHH9c777yjnJwclZSUqKKiosY/9bFixQpJ0ogRI6qci4qKUmJiosrKyrRq1ao6y4qIiJDNZtOgQYOqPd+1a1dJ0sGDB+vRYv+Vkpyg3nFtXbq2d1xbTUpO8HKL/BObNgMAAKAupk4XvO6661RaWqoTJ07oueee03PPPVfr9fXdJyszM1OSFB8fX+35bt26aeXKldq2bZuuuOKKWsuaNm1arec3bNggSerYsaP7DQ0A4WFWTUtJqnEanHRyimBjngbHps0AAABwhalBVnp6uvPvhuHe5raeOHTokCTpjDPOqPZ8hw4dKl3nqRUrVujnn39WWFhYtaNmZjEMQ3a776aXhYZIt45O0LUjbFq+LkebsvJUXFKuiPBQ9Y5rqxEDYtS6xcmU/N5o56ll+vJ9qMnStXs82rR56do9Gju0u5da5Z/8vS/hGvoxeNCXwYO+DB70pX+rbyxjapD17rvvmllcnQoLCyWdnOpXHcdxx3We2L59ux555BFJJ9drde7c2eOy6lJUVFQpUPWl7m2k7m1OfV9PaE/W1garPyPD/6bZrdngfgZGSVqTvkfd25wwuTWBwx/7Eu6jH4MHfRk86MvgQV8GH1ODrIEDB5pZXJ2sVqsqKipksVhqvc7TSHTjxo2aNGmSjh49qksuuUR33nmnR+UgOJSWefY58vQ+AAAABKaA3k22WbNmOnr0qIqKiqo9X1xcLEmKjIx0u+yvvvpKDz/8sIqKijR8+HC9+OKLCgkxNU9IFZGRkTWuL2sM7Ha785uchIQEWa3+te6r7bq12pd32P372rRU3759zW+QH/P3voRr6MfgQV8GD/oyeNCX/m379u01xhiu8EqQlZWVpe3bt6u4uLhKBkG73a6ioiIdPHhQ3377bb3SvXfo0EFHjx5Vbm6uunTpUuW8Yy2WY22Wq2bPnq1XXnlFhmHoT3/6kx577LEG+eBbLBZ+wP6P1Wr1u/ei59lttGGH+0FWn27t/e61NCR/7Eu4j34MHvRl8KAvgwd96X/qmilXF1ODrIqKCj300EP6/PPP67zWMIx6Nz4+Pl6ZmZnauXOn+vXrV+X8zp07nde5oqKiQo8++qg++eQTWa1WPfzww7r++uvr1UYEh5Iyu9K3u78mqzFv2gwAANBYmRpkLVq0SJ999pkkKSwsTK1atdLhw4fVqlUrhYeHKz8/X2VlZbJYLOrZs6cmTJhQr/qGDBmizz77TEuXLtW4ceMqncvPz1daWprCwsI0ePBgl8p77LHH9Mknn6hp06Z66aWXNGTIkHq1D/4tv6BYy9KytSkrT0Ul5YoMD1VCXDuNSKy60fK8xRnalp3vdh2NddNmAACAxszUIOvzzz+XxWLRhAkT9NBDD6m4uFiJiYkaMmSI/vrXv6q0tFSLFy/WjBkzdOjQIV188cX1qm/EiBHq1KmTVq9erffff1/jx4+XdHIt1tSpU1VYWKhrr71W7dq1c95TVlamnJyTex3FxMQoLCxMkrR48WItWrRIoaGhmjNnjpKSkurVNvivkjJ7jfuBpWfmasGybZX2A/NkfyxJ6hEb1Wg3bQYAAGjMTA2ytm/frsjISN17772yWq1q1qyZbDab0tLSJElNmjTRuHHjVFpaqqefflrz58/XXXfd5XF9EREReu655zRp0iRNnz5dixYtUnR0tNavX69Dhw6pV69euv/++yvdc/DgQY0aNUqSlJqaqujoaNntdr300kuSpLZt22rRokVatGhRtXVeeOGFSk5O9rjN8K2SMrumzVujTVl5NV5zcm+rbKVn5mrGrRfom5/3ub0/liT169HB7zdtdmc0DwAAAK4xNcg6fvy4unbtWmnfqm7dumnr1q06cuSI2rRpI0kaN26cZs6cqW+++aZeQZYkJSYmauHChXr11Vf1ww8/aOfOnYqOjtaYMWN00003qXnz5nWWsX37du3fv1/SySDMMeWxOi1btiTICmDzFmfUGmCd6uCRQqU887XatPQs2Niy+4hH9zUEd0fzAAAA4DpTg6zIyMgqac5jYk4u+t+1a5czyGrSpIliY2O1d+9eU+q12WyaNWuWS9dGR0dr+/btlY716tWryjEEH0+m/RmGlPdbsUf1FZWUe3Sft7kzmvdL7nFNS0lSOIEWAACAy0zd+KlTp07au3evSkpKnMeio6NlGEaVIKa0tFSlpaVmVg/UatkP2R5N+/NUZLh/bkPnzmjepqw8zVvMLvQAAADuMDXIGjhwoAoLC/XXv/7VuT9Wz549JZ1MimEYJx9ws7KytGfPHnXs2NHM6oFauRpYmCUhrl3dFzUwT0bzUtflKL/As9E8AACAxsjUIGvChAkKCwvTBx98oIsvvlilpaWKj49Xz549lZ6erptuuknPP/+8brjhBlVUVOi8884zs3qgVg05fc9f98fyZDSv3G5oeZr72RUBAAAaK1ODrJiYGM2cOVMtWrTQiRMn1KRJE0nS3XffLYvFojVr1uif//yncnNzFRERodtvv93M6oFaNeT0PX/dH8vT0byMrMMmtwQAACB4mf7UOXz4cA0cOFA//vij89iQIUP01ltv6Y033tC+ffvUtWtX3X777c6kGEBD6B3XVumZuQ1Sj7/uj+XpaJ6/JvEAAADwR175ar9ly5YaOnRopWODBg3SoEGDvFEd4JJLB8bqw2Xb65X8oml4qIpKylVdCaFWi9+nPfd0NM9fk3gAAAD4I1OnCwL+LKplhIYNqN/oaWE1AZZFUvcurTX34eGaMrav3wZY0slRNk/4YxIPAAAAf+Xx19Mvv/yyJGnixIlq3bp1pWPuqO9mxIA7UpIT9EvucVMzDRqSduw9qhc//Nnv95TyZDTPX5N4AAAA+CuPg6zXXntNFotFV155pTPIchxzhWEYslgsBFloUOFhVk1LSdK8xRlalpYtw8Rtsxx7Sk0Z29e8Qk3mGM1bujbb5Xv8NYkHAACAv/I4yBowYIAkKTIyssoxwJ+Fh1k1ZWxfjR1m06OvrdahI0WmlZ26LkfjL+vh10GJO6N5/pzEAwAAwF95HGTNnz/fpWOAvzqjTVPNeXCY5i3OUOq6nHolxHBw7Ck1brjNhBZ6x6mjeTW97kBI4gEAAOCvSBmGRs0xqjX+sh5anpajjKzDKiopV2R4qA7mndD+vEK3y8zIOuzXQZZU++tOiGunEYlMEQQAAPAUQRYgKapFhMYNt1UKju6f9V+Pgiyz9pTKLyjWsrRsbcrK81oAVN3rBgAAQP14HGSNHz++3pVbLBa999579S4H8AZf7SlVUmavcSpfemauFizbxlQ+AAAAP+bx0+BPP/1U4zlHhkGjmtRtp55zNRMh4Au949oqPTPX7fvqs6dUSZld0+atqTUpRbnd0NK12fol97jfp4wHAABojDwOsqZMmVLt8e+//14///yzWrdurVGjRqlnz55q2bKliouLtXPnTn3++ec6cOCARowYoREjRnjccMDbfLGn1LzFGS7v4RUIKeMBAAAaI1ODrE2bNun111/XwIED9eqrr6ply5ZVrrn99tt11113KTU1VX/60588rR7wuobeUyr/WLFS1+W4dU8gpIwHAABobExNfPHKK6/IYrHohRdeqDbAkqSIiAg999xzuvjiizVnzhwlJSWZ2QT4gYZI2NBQGnJPqWU/ZLudRj4QUsYDAAA0NqYGWT///LO6d++u9u3b13pdVFSUunfvrs2bN5tZPXwsGBM2NOSeUq5OEzxdIKSMBwAAaExMT+F+4sQJl647cuSImjRpYnb18JFgTtjQUHtKeZr63ayU8QAAADCHqUFWXFycNmzYoLVr12rQoEE1XvfFF1/o119/ZapgEGkMCRu8vaeUr1LGAwAAwFwhZhY2duxYGYahO++8U//5z39UVlZW6XxxcbHmz5+vRx99VBaLRdddd52Z1cNHPE3YkF9Q7KUWBabecW09uq8+KeMBAABgPlO/Ah89erRWrFih1NRUPfzww3ryyScVExOjpk2b6sSJE9qzZ4/Ky8tlGIauu+46DRs2zMzq4SOBnLDBn5J0+CJlPAAAAMxnapBlsVj00ksv6Y033tBbb72lgoICZWZmVrqmbdu2uuuuuzRu3Dgzq4YPBWLCBn9M0tHQKeMBAADgHaYv5ggLC9Ott96qlJQU/fjjj9q9e7eOHTum1q1b6+yzz1b//v0VEmLqLEX4WKAlbPDnJB0NmTIeAAAA3uG1FfOhoaEaNGhQrQkwEBwCLWGDPyfpaMiU8QAAAPAOrz3l/vbbb1qzZo127dqlgoICPfTQQyopKdGGDRs0cOBAb1ULH+gd11bpmblu3+eLhA2eJukYf1mPBpuW11Ap4wEAAOAdpgdZhmHolVde0dtvv63i4v9lj3vooYe0b98+TZw4UX379tXs2bPVpk0bs6uHDwRSwoZAStLh7ZTxAAAA8A7TF0c9+OCDeu2111RUVKTWrVsrMjLSee7o0aMyDEPp6emaMGGCioqKzK4ePuBI2OAOXyVsqE+SDgAAAMAVpgZZy5Yt02effaY2bdpo3rx5WrNmjXr06OE8f/755+v9999X27ZttWvXLr377rtmVg8fSklOcHmfJ18mbAi0JB0AAAAIPKYGWR999JEsFoteeOEF/e53v6v2mvPPP18vvfSSDMPQ0qVLzawePuRI2DByUKxCrZZqrwm1WjRyUKyeSknyWcKGQEvSAQAAgMBj6pPjpk2bdOaZZ9aZUbB///7q3Lmz9uzZY2b18LFASNgQSEk6AAAAEJhMDbIKCwvVuXNnl65t06aNcnPdf9iF//PnhA2BlKQDAAAAgcnUIKtdu3bKzs6WYRiyWKqfMiZJZWVl2rNnj9q1Y3QADcuRpGPp2myX7zk9SUd+QbGWpWVrU1ae343UAQAAwPdMDbIGDhyoTz/9VB988IHGjx9f43XvvPOOCgoKdMkll5hZPeCSlOQE/ZJ73KVMg6cm6Sgps9e4SXB6Zq4WLNvGJsEAAAAwN/HFjTfeqJCQED3//PN69913lZ+fX+l8Xl6eXnrpJc2cOVMhISG67rrrzKwecIknSTpKyuyaNm+Nlq6teZ+tcruhpWuz9eS8NSops3vzJQAAAMCPmTqS1aNHDz366KN6+umn9eyzz+rZZ591nktKStLRo0clndyw+K677lKfPn3MrB5wmbtJOuYtznB5j61NWXmatzhDU8b29VLrAQAA4M9Mz0s9fvx4denSRTNnztS2bducxx2jWrGxsbrrrrs0atQos6sG3OZKko78Y8VKXZfjVrmp63I0/rIerNECAABohLyy+c9FF12kiy66SL/88ot27NihgoICRUZG6uyzz1ZcXJw3qgS8ZtkPNU8RrEm53dDytBy/zLAIAAAA7/LqDqudO3d2OaU74K9cnSZ4uoyswwRZAAAAjZCpiS+AYFRUUt6g9wEAACCweTySdfHFF9e7covFopUrV9a7HMCbIsM9+zHx9D4AAAAENo+fAg8cOCCLxSLDcG+tyqlq27AYwSEYNu7tHddW6Zm5bt+XEMdm2wAAAI1Rvb9qt1gs6tmzpy6//HK1a8dDJU4Kpo17Lx0Yqw+XbXcr+UWo1aIRiTFebBUAAAD8lcdB1gMPPKAvv/xSmzZt0pYtW7R9+3YNHDhQo0aN0siRI9WyZUsz24kA4ti4t7aEEY6Ne3/JPa5pKUkK9+NAK6plhIYNiNHStdku3zNsQOCM1AEAAMBcHie+uOmmm/Svf/1Ly5cv1z333KPu3btrzZo1euKJJzR48GDdcsst+uyzz1RYWGhmexEAPNm419+lJCeod1xbl67tHddWk5ITvNwiAAAA+Kt6Zxfs0qWLJk+erMWLF+vLL7/U7bffrpiYGH3zzTd68MEHdcEFF+juu+/W8uXLVVpaakab4cc83bg3v6DYSy0yR3iYVdNSkjRyUKxCrdWvJQy1WjRyUKyeSkny+ymQAAAA8B5T05+dffbZmjJliqZMmaLMzEwtWbJEX331lb766istXbpUTZs21fDhwzVq1ChdeOGFslp5EA029dm494+X+PdG1eFhVk0Z21fjL+uh5Wk5ysg6HLDJPAAAAOA9XssxbbPZZLPZdM8992jLli1asmSJvvzyS/3nP//Rp59+qpYtW2rkyJGaPn26t5oAH6jPxr3+HmQ5RLWI0LjhNjYaBgAAQLUaZDPiXr166YEHHtCKFSv0+OOPq2nTpvrtt9+0cOHChqgeDYiNewEAANDYNchuqRs2bHBOGdy/f79zb63OnTs3RPVoQGzcCwAAgMbOa0+26enpzsDqwIEDzsCqU6dOGjlypC6//HL16dPHW9XDR9i4F/CuYNjgGwCAYGdqkLV+/Xp99dVXWrZsWaXA6swzz3QGVueee66ZVcLPsHFvYOMB3n8F0wbfAAAEu3oHWT///LMzsDp48KAzsDrjjDOcgdV5551X74YiMNRn41673e7FlqE2PMD7t2Db4BsAgGDncZA1Y8YMLVu2TIcOHXIGVu3bt9fIkSN12WWXqX///qY1EoElJTlBv+QedynTIBv3+h4P8P7Pkw2+p4zt691GAQCAGnkcZM2fP18Wi0VWq1WDBg3S5Zdfrv79+ysk5GTCwr1797pUTpcuXTxtAvyUY+PemkZGpJNTBM0aGWGK20mevg88wPs3Tzf4Hn9Zj0b1+QcAwJ/Ue7qg3W7Xd999p++++87tey0Wi7Zs2VLfJsAPNcTGvUxxO6k+7wMP8P6vPht8s5cbAAC+Ua8gyzFN0Ff3w/95a+NepridVN/3gQd4/1efDb7pIwAAfMPjICs1NdXMdgBuYYrbSfV9H3iA939s8A0AQODxOMhiI2H4ClPcTjLjfeAB3v+xwTcAAIEnxNcNANxVnyluwcSM94EHeP/XO66tR/exwTcAAL7DkxICjqdT3H7efkiGjKDJQmjGVL/ecW2Vnpnrdhk8wDccNvgGACDwEGQh4Hg6VW3zrjxt3lU5MAnkLIRmTPXjAd7/1WeDbwAA4BtMF0TAMXuqmiP73pPz1qikzG5q2d5kxlQ/xwO8O3iAb3gpyQkuTxtkg28AAHyPIAsBx9M1KnVxZN8LFGat1eEB3v85NvgeOShWoVZLtdeEWi0aOShWT6UkBdSILAAAwYjpggg4nkxxc1UgZSE0a6qf4wG+pg2NHfcF4pTKYNIQG3wDAABzEGQh4HiyRsVVp260m19QrGVp2X6bKMPMtTo8wAcOb23wDQAAzEOQhYCUkpygX3KPe5xhrzYbduTqUH5htaM6/pYow533wZWpfjzAAwAA1B9rshCQXFmj4qkde49q6dqa96Dyp0QZrNUBAADwP4xkIWDVNcVt/fZD2rTL/ZEuV1OjOxJlTBnb1+06zMRUPwAAAP9CkIWAV9MUN0OGR0GWO/wpUQZT/QAAAPwD0wURtC4daP5UwtM5EmUAAAAADgRZCFqebLTriYysw16vAwAAAIGDIAtBzZ2NdiPDPZs96+oaLgAAADQOBFkIau5k37PFtPaoDk+DMwAAAAQnng4R9FzNvvfR19u1YYf7U/8S4tp5odUAAAAIVIxkoVExVP3eV5JniTJCrRaNSPT+ui8AAAAEDkayEPRKyuyatzhDqetyqmwwnJ6ZqwXLtmnYgBhNSk7QsAExWro22+Wyhw1gDyp/l19QrGVp2dqUlcf+YQAAoEEERZC1e/duzZ49Wz/99JPy8vLUsWNHXX755Zo8ebKaNm3qcbn5+fn6/e9/r3POOUdz5841scVoKCVldk2bt0absmreL6vcbmjp2mz9kntcj9wwUL/kHq/1eofecW01KTnBzObCRO4E103CrD5qJQAACEYBP11w48aNGj16tD777DO1a9dOF198sQoLC/X666/rmmuu0fHjxz0qt7CwUHfccYdyc3NNbjEa0rzFGS4FTJK0KStP7y7Z4nKijKdSkng491OO4Hrp2uwqAZaDI7h+ct4alZTZG7iFAAAgmAX0SFZ5ebnuvfdeFRYWasaMGRozZowkqbi4WPfcc49WrFihmTNn6oknnnCr3L179+qee+5RRkaGN5qNBpJ/rFip69zbKDh1XY7GX9bDpUQZHrWJqWsNwt3get7iDE0Z29e7jQIAAI1GQAdZS5Ys0d69e5WUlOQMsCQpIiJCzzzzjIYOHaqPP/5Yd911l1q1alVnecXFxXr33Xc1d+5cHT9+XF26dNHevXu9+RLgRct+qHkUoybldkPL03I0brhNUS0iNG64TeOG2+rdFqauNZz6BNcEugAAwAwBPV1wxYoVkqQRI0ZUORcVFaXExESVlZVp1apVLpX3xRdf6IUXXlBoaKhmzJih2267zdT2omG5OpJxuows99O414apaw2rPsE1AACAGQI6yMrMzJQkxcfHV3u+W7dukqRt27a5VF7r1q112223afny5ZVGxhCYikrKG/S+mngydQ2e85fgGgAANF4BPV3w0KFDkqQzzjij2vMdOnSodF1dhg4dqqFDh5rTOA8YhiG7vfGOYpz62s14HyKaeDbtLqKJ1bR+yC8o1tduTl37el2Orh1hU+sW4aa0wRfM7kt3FBWXeXxfY/75q44v+xHmoi+DB30ZPOhL/2YY7s2KOV1AB1mFhYWSTq7Bqo7juOM6f1dUVKT09HRfN8MvmJF0pG1kqWf3NS01rR/+u+mY7G5OXbPbDb37aZouOqelKW3wtYZOIFNWWuTxffz81YxEQMGDvgwe9GXwoC+DT0BPF7RaT45UWCzVp9p2qG8kisB0Xlwzhbj5CQ8Jkfp1bWZaG/YcKvHsvoOe3QfprA6ejQCedUbgjhwCAAD/EtAjWc2aNdPRo0dVVFT9N9fFxcWSpMjIyIZslsciIyNrXF/WGNjtduc3OQkJCc4guj42/rJRy39wfbre8AExujCpT73rdViwerUk9wOmsCaR6tu3r2ntaGje6EtXxcYV69vNqW6NIFqtFl1/ZWJAT9H0Bl/2I8xFXwYP+jJ40Jf+bfv27TXGGK4I6CCrQ4cOOnr0qHJzc9WlS5cq5x1rsRxrs/ydxWLhB+z/WK1WU96LyaP7aH/eCZeSIfSOa6vJV/UxtQ8iI8I8vi9YPgtm9aWr2rVupuEDYrR0bbbL9wwfEKO2rZt6sVWBr6H7Ed5DXwYP+jJ40Jf+p66ZcnUJ6OmCjlGfnTt3Vnvecbwxjw41duFhVk1LSdLIQbEKtVb/wxJqtWjkoFg9lZJk+h5VvePaenRfQlw7U9vR2KQkJ7j83veOa6tJyQlebhEAAGhMAjrIGjJkiCRp6dKlVc7l5+crLS1NYWFhGjx4cEM3DX4kPMyqKWP76q3HL9WEy3uqr6294mOj1NfWXhMu76m3Hr9UU8b29comwJcOrDm4q0mo1aIRiTGmt6Ux8XVwDQAAGreAni44YsQIderUSatXr9b777+v8ePHSzq5Fmvq1KkqLCzUtddeq3bt/jcqUFZWppyck2t0YmJiFBbm2XQuBJ6oFhEaN9ymccNtDVdnywgNc3Pq2rABMYpqUTljZn5BsZalZWtTVp6KSsoVGR6qhLh2GpFY9Vqc5Aiux1/WQ8vTcpSRdZj3DgAANIiADrIiIiL03HPPadKkSZo+fboWLVqk6OhorV+/XocOHVKvXr10//33V7rn4MGDGjVqlCQpNTVV0dHRvmg6GpGU5AT9knvc5XVhp05dKymza97iDKWuy1H5aYkc0jNztWDZNg0bEKNJyQmMxtTAF8E1AABo3AJ6uqAkJSYmauHChRo5cqR+/fVXffPNN2rRooVuu+02zZ8/X82bN/d1E9HIeTp1raTMrmnz1mjp2uwqAZZDud3Q0rXZenLeGpWUsZEhAACAPwjokSwHm82mWbNmuXRtdHS0tm/f7tK1o0eP1ujRo+vTNECSZ1PX5i3OcGn0S5I2ZeVp3uIMTRnb1wutBwAAgDuCIsgCAoWrU9fyjxUrdZ3r+3tJUuq6HI2/rAfrjAAAAHws4KcLAsFo2Q81TxGsSbnd0PI09wIzAAAAmI8gC/BDrk4TPF1G1mGTWwIAAAB3EWQBfqiopLxB7wMAAIB5CLIAPxQZ7tlySU/vAwAAgHkIsgA/1DuurUf3JcS1q/siAAAAeBVBFuCHLh1Y855aNQm1WjQiMcZLLQIAAICrmFsEmCS/oFjL0rK1KSuvzj2w6hLVMkLDBsRo6dpsl+8ZNsD9egAAAGA+giygnkrK7Jq3OEOp63KqpF1Pz8zVgmXbNGxAjCYlJ6hJmNXlclOSE/RL7nGXMg32jmurSckJbrcdAAAA5iPIAuqhpMyuafPW1BoIldsNLV2brV9yj2taSpLCXQy0wsOsmpaSVGMAJ52cIuhJAAf4kpmjvgAA+COCLKAe5i3OcHlPq01ZeZq3OENTxvZ1ufzwMKumjO2r8Zf10PK0HGVkHeahFAHLW6O+AAD4G4IswEP5x4qVui7HrXtS1+Vo/GU93F+j1SJC44bbNG64za37AH/hzVFfBD5GNwEEG4IswEPLfsiudgpfbcrthpan5RAsodHx9qgvAhOjmwCCFSncAQ+5+sB4uoyswya3BPBvno765hcUe6lF8AeO0c2la2v+wsoxuvnkvDUqKbM3cAsBwHMEWYCHikrKG/Q+IFDVZ9QXwcuT0U0ACBRMFwQ8FBnu2Y+Pp/e5gnUN8Ef1GfVlam1wasg1rQDgCwRZgId6x7VVemau2/clxLUzvS2sa4A/Y9QXp2NNK4BgR5AFeOjSgbH6cNl2tx4UQq0WjUiMMbUdZG2rihE9/+KPo77wLUY3AQQ7/gUDPBTVMkLDBsRo6dpsl+8ZNsD8h3yytv0PI3r+yZ9GfeEfGN0EEOxIfAHUQ0pygnrHtXXp2t5xbTUpOcHU+sna9j9kKvNflw6MVajV4tY93hj1hf9gdBNAsCPIAqqRX1Csj77ersfnfq/7Z/1Xj8/9Xh9/nVklOAkPs2paSpJGDqr5ITLUatHIQbF6KiXJ9NETsrb9D5nK/Jdj1Ncd3hj1hf9w9cup0zG6CSBQ8JUQcApPppuFh1k1ZWxfjb+sh5an5Sgj63CDrQNiXcNJwZipLNjWlaUkJ+iX3OMufWa9MeoL/+Iva1oBwFsIsoD/U98EElEtIjRuuK1BgxfWNZwUTJnKgnVdmWPUt6bXJp18iA7E1wb3+cuaVgDwFoIs4P+YkUCioUcfWNdwUrCM6AV7pkhfjvrC/zC6CSCYBdeTFuCh+k4389XoA1nbTgqWEb3GkinSF6O+8D+MbgIIZgRZgOo33ewPQ+J8NvrAuoaTgmFELxjXlQF1YXQTQLDynycMwAtcnb5Xn+lmh/ILfTb6wLqGk4JhRC+Y1pUB7mJ0E0CwIchCUHJ3+p6n08YKCku1KeuwW/eYPfrAuobgGNELlnVlAACAfbIQhDzZlNbTaWOFRWU+36fK13t1+YNg2IcpWNaVAQAARrIQhNxNHvDI7NXq072dR9PNPGX26APrGgJ/RC8Y1pUBAICT+NcZQcWT5AE79h5V1r6jslgkw41BqVCrRU0jw9xs4UneGn1ozOsaAj1TWTCsKwMAACcRZCGoeJI8QJIq3L9FwwbE6OCRQvdvFKMP3hLII3rBsK4MAACcxJMegoqnyQPc5Zhu9sm3Oxl98EOBOKJHpkgAAIIHiS8QVMyYhmexSNYafjJOTyBx6cCak03UhNEH1CQlOUG949q6dK0/risDAAAnMZKFoGLGNDzDkK66pLsiw0PrnG7myehDs8gwzfzgZ7+fvoaGF+jrygAAwEkEWQgqniYPON3OfUf1l8kXuDTdzJ2sdpL02/FSpWfmVrtfFxDI68oAAMBJBFkIKp4kD6iOO9MOXRl9qIljv65fco9rWkqSwgm08H8CcV0ZAAA4iSALQcWT6XvVcXfa4emjDz9vP6Sd+46qpNTu0v2bsvI0b3GGpozt60FrASm/oFjL0rK1KSuPkS8AAHyMIAtBx93pe9XxNPtf04gwHcov1LY9R2R3My986rocjb+sBw/EcEtJmb3GUVSmpAIA4BtkF0TQcUzfGzkoVtYQ9zL/SZ5n/ysps2vavDVaujbb7QBLOjl1cHmaexspo3E79TNX0zRVx5TUJ+etUUmZayOrAACgfhjJgk9UN7XpnK5tdGakXc0j6/9t+6nT9/7yZpp27D3q8r2e7j00b3FGvffpysg6zBocuMydzxxTUgEAaDgEWWhQdU1tCgmRzuvaTL3OsSvSWv9gK6pFhJ69/UJNm7fGpYdRT/ceyj9WrNR19R+FMmOfLzQOnnzmmJIKAEDDYLogGowrU5sqKqSfdp7QU2+mmTa16dTpgzVtHHz6JsPuWvZDza/JHWbs84XGwZPPHFNSAQBoGDzRocG4M7Vp864jpk5t8sbeQ6dOeczMyTelne4m3CCjXOPl6dRUpqQCAOB9BFloEP4ytcmMvYdqm/JYH+4k3CCjHDydWsqUVAAAvI/pgmgQwTK1yZUpj55yNeEGGeUgeT61lCmpAAB4H0EWGkR9pjb5EzMyCFbHnYQbnmSUQ/DpHdfWo/s83QMOAAC4jiALDSIYpjaZlUHwVNYQuZVww9Npl/kFxZ42EX7q0oE1J3Kpiad7wAEAAPcQZKFBBMPUJrMyCDp079Jabz8xUlPG9nV53VSwTLtE/UW1jNCwAe4FTJ7uAQcAANzjP0+wCGq949oqPTPX7fv8aWqTmdMEe8e19ShdPBnlcKqU5AT9knvcq3vAofEgWykAmIcgCw3i0oGx+nDZdrdGYfxtapMZUxdDrZZ6Zf0LhmmXMI9jD7jasl3W9zOH4Ee2UgAwH0EWGoRjatPStdku3+NvU5s8nbrYNCJUtpgoU74RDoZplzCXN/aAQ+PhyFZa22ioI1vpL7nHNS0lSeEEWgBQJ5680GDcmdp0Ttc2fje1ydMpj3+8pLtpU/WCYdolvMOMPeDQ+HiSrdSsTeIBIJiR+AINxjG1aeSgmrOihYRI53drpidvSvS7aSn+kM3NH9oAIDiQrRQAvIeRLDSo2qY2ndO1jc6MPKbmkVa/C7Ak/5jy6A9tABAc6pOtlBFTAKgdQRZ8orqpTXa7Xenp6b5rlAv8IZubP7QBQOAjWykAeA/TBQE3uDLlMdRqcWuD4UBsA4DAR7ZSAPAeRrIAN/lDNjd/aAMqY48hBBqylQKA9/CbEvCQP2Rz84c2NHbsMYRARbZSAPAepgsCgIccewwtXVtzAgHHHkNPzlujkjJ7A7cQqBnZSgHAexjJQqPDtC6YhT2GEMjIVgoA3kOQhUaDaV0wk6d7DI2/rAcPqfAbZCsFAO9guiAaBaZ1wWz12WMI8BdkKwUA72AkC40C07pgNvYYQrAgWykAmI8gC0GPaV3wBvYYQrAhWykAmIfpggh6TOuCN7DHEAAAqAlBFoJefaZ1ATXpHdfWo/vYYwgAgOBHkIWgx7QueAN7DAEAgJoQZCHoMa0L3uDYY8gd7DEEAEDjQJCFoMe0LnhLSnKCy58v9hgCAKDxIMhC0GNaF7yFPYYAAEB1mA+FoOeY1rV0bbbL9zCtC65ijyEAAHA6giw0CinJCfol97hLmQaZ1gVPsMcQAABwYLogGgWmdQEAAKChMJKFRoNpXQAAAGgIBFlodJjWBQAAAG8iyAIAAAhC+QXFWpaWrU1ZeczcABoYQRYAAEAQKSmza97iDKWuy1G53ah0Lj0zVwuWbdOwATGalJzAGmTAS4IiyNq9e7dmz56tn376SXl5eerYsaMuv/xyTZ48WU2bNnWrrIMHD2rOnDn6/vvvdeDAAbVr105Dhw7V7bffrjZt2njpFQAAANRfSZld0+atqTWbbrnd0NK12fol97impSQpnEALMF3AB1kbN27UxIkTVVhYqD59+ighIUE///yzXn/9da1cuVIffPCBmjdv7lJZe/fu1bXXXqvc3FzZbDZdcskl2rJli9577z19/fXX+uijj9SxY0cvvyL4I6ZcAAACwbzFGS5tVyJJm7LyNG9xhqaM7evdRgEuCLZnrYAOssrLy3XvvfeqsLBQM2bM0JgxYyRJxcXFuueee7RixQrNnDlTTzzxhEvlPfzww8rNzdXtt9+uO++8U5Jkt9s1ffp0ffjhh3ryySc1d+5cr70e+B+mXAAAAkX+sWKlrstx657UdTkaf1mPgHyIRXAI1metgN4na8mSJdq7d6+SkpKcAZYkRURE6JlnnlHTpk318ccf67fffquzrB9//FE//vijzjrrLE2ZMsV53Gq16rHHHlOnTp30zTffaOfOnV55LfA/jikXS9dmV/mhd3BMuXhy3hqVlNkbuIUAAPzPsh9q/veqJuV2Q8vT3AvMALME87NWQAdZK1askCSNGDGiyrmoqCglJiaqrKxMq1atcrmsYcOGKSSk8tsSFhamoUOHSpJSU1Pr22wECE+mXAAA4Cuu/pt1uoyswya3BHBNMD9rBXSQlZmZKUmKj4+v9ny3bt0kSdu2bXO5LJut+r2T3CkLgc/TKRf5BcVeahEAALUrKilv0PuA+gj2Z62AXpN16NAhSdIZZ5xR7fkOHTpUuq6hyvKUYRiy2wNnGNRsp752X78PS9fu8WjKxdK1ezR2aHcvtSpw+FNfwnP0Y/CgL4NHbX0Z0cSz9SoRTax8Lnygsf9c+vuzlmG417bTBXSQVVhYKOnkGqzqOI47rnOlrMjIyHqX5amioiKlp6d7rfxAkpHh2+HgNRtyPbsvfY+6tzlhcmsCm6/7EuagH4MHfRk8Tu/LtpGlHpXTtmkpzx8+1hh/LoP9WSugpwtarSe/sbFYLLVe50okamZZCHylZZ71s6f3AQBQX+fFNVOIm092ISFSv67NvNMgoBbB/qwV0CNZzZo109GjR1VUVFTt+eLik3M2axqdOr0sSaaU5anIyMga15c1Bna73flNTkJCgjPw9YW269ZqX577C4Hbtmmpvn37mt+gAONPfQnP0Y/Bg74MHnX15cZfNmr5D66vcxk+IEYXJvUxtY1wTWP/ufT3Z63t27fXGBe4IqCDrA4dOujo0aPKzc1Vly5dqpx3rJ9yrKeqq6zNmzcrN7f6oUt3yvKUxWJpdD9gNbFarT59LxK6tdOGHe7/4Pfp1p4+PI2v+xLmoB+DB30ZPKrry8mj+2h/3gmXMrb1jmuryVf14fPgBxrjz6W/P2vVNbutLgE9XdAx6lPT3lWO466MDplZFgLfpQNjFWp174cr1GrRiMQYL7UIAIC6hYdZNS0lSSMH1fzvWKjVopGDYvVUSlJAbe6K4BLsz1oBHWQNGTJEkrR06dIq5/Lz85WWlqawsDANHjzY5bKWL1+uioqKSufKysqc+2Ndcskl9W02AkBUywgNG+DeD/GwATGKalF9EhYAABpKeJhVU8b21VuPX6oJl/dUX1t7xcdGqa+tvSZc3lNvPX6ppoztS4AFnwr2Z62Ani44YsQIderUSatXr9b777+v8ePHSzq5fmrq1KkqLCzUtddeq3bt2jnvKSsrU07OybnKMTExCgsLkyT169dPCQkJysjI0MyZM3XffffJYrHIbrdrxowZ2r9/vy666CL17Nmz4V8ofCIlOUG/5B53ecrFpOSEBmgVAACuiWoRoXHDbRo3vPo9QAFfC+ZnrYAOsiIiIvTcc89p0qRJmj59uhYtWqTo6GitX79ehw4dUq9evXT//fdXuufgwYMaNWqUJCk1NVXR0dHOc88++6yuu+46zZs3T6mpqerevbu2bt2qnJwcde7cWU8//XSDvj74lmPKxbzFGUpdl1PtXg6hVouGDYjRpOQEvhEEAABwQzA/awV0kCVJiYmJWrhwoV599VX98MMP2rlzp6KjozVmzBjddNNNat68uctlde/eXf/+97/16quvatWqVVq5cqU6duyoCRMm6JZbbqk0IobGwTHlYvxlPbQ8LUcZWYdVVFKuyPBQJcS104jEwBm2BgAA8DfB+qwV8EGWJNlsNs2aNcula6Ojo7V9+/Yaz3fu3FnPPvusWU1DkGDKBQAAgPcE27NWQCe+AAAAAAB/Q5AFAAAAACYiyAIAAAAAExFkAQAAAICJCLIAAAAAwEQEWQAAAABgIoIsAAAAADARQRYAAAAAmIggCwAAAABMRJAFAAAAACYiyAIAAAAAExFkAQAAAICJCLIAAAAAwEQEWQAAAABgIoIsAAAAADCRxTAMw9eNaOzS09Nlt9tlsVgUGRnp6+b4jGEYKioqkiRFRkbKYrH4uEXwFH0ZHOjH4EFfBg/6MnjQl/6tqKhIhmHIarWqb9++bt8fan6T4K6KigpJJ3/YCgsLfdwa/+D4pYPAR18GB/oxeNCXwYO+DB70pf9yPKe7iyDLD4SFhamsrEwhISEKDw/3dXMAAACARq2kpEQVFRUKCwvz6H6mCwIAAACAiUh8AQAAAAAmIsgCAAAAABMRZAEAAACAiQiyAAAAAMBEBFkAAAAAYCKCLAAAAAAwEUEWAAAAAJiIIAsAAAAATESQBQAAAAAmIsgCAAAAABMRZAEAAACAiQiyAAAAAMBEBFkAAAAAYCKCLAAAAAAwEUEWAAAAAJiIIAsAAAAATESQBQAAAAAmCvV1AxA4du/erdmzZ+unn35SXl6eOnbsqMsvv1yTJ09W06ZN3Srr4MGDmjNnjr7//nsdOHBA7dq109ChQ3X77berTZs21d6TkZGhOXPmaNOmTTp27Ji6dOmi5ORkTZw4UWFhYVWuP3LkiN544w1988032rdvn0JCQhQbG6vLL79cN9xwgyIiIjx6H4JBoPXl6fbv368//OEP+u2337R9+3a32htsArEvf/zxR7311ltav369CgoK1L59ew0ZMkRTpkxRu3bt3H4PgkWg9WVJSYneeOMNffHFF8rJyVFoaKi6d++usWPHasyYMbJYLB69D4HO1/14qh9//FETJkzQ/fffr5tuuqnG67744gu9++672rVrl+x2u3r06KGJEyfq0ksvdau9wSbQ+pLnHv9iMQzD8HUj4P82btyoiRMnqrCwUH369NGZZ56pn3/+Wbm5uYqPj9cHH3yg5s2bu1TW3r17de211yo3N1c2m01nn322tmzZor1796pjx4766KOP1LFjx0r3rFy5UlOmTFFFRYX69++vli1bat26dfrtt980ePBgzZ07t9JDwN69ezV+/HgdPHhQbdq0UUJCgkpLS7VhwwYVFhbqnHPO0bvvvutym4NJoPXl6SoqKjRx4kT98MMPktSog6xA7Mt3331XzzzzjCTpvPPOU+vWrbVx40YdPnxY0dHR+te//qWoqChz3qAAEmh9WVxcrIkTJyo9PV2tWrXSeeedp9LSUq1bt05lZWUaNWqUXnzxRVPfo0Dg63481e7duzVhwgTl5ubqwQcfrPHB/G9/+5veeOMNNW3aVImJiSotLdUPP/ygsrIyTZkyRXfccYdH70WgC7S+5LnHDxlAHcrKyoxhw4YZNpvNWLhwofN4UVGRccsttxg2m8146qmnXC7vT3/6k2Gz2YyXX37Zeay8vNx44oknDJvNZkyaNKnS9UePHjX69etn9OrVy1i1apXzeH5+vjF27FjDZrMZb7zxRqV7Jk6caNhsNuOuu+4yTpw44Tx+4MAB46qrrjJsNpvx5JNPutzmYBGIfXm62bNnGzabzfmnsQrEvtywYYPRo0cP47zzzjPWrFnjPH7ixAlj8uTJhs1mM6ZOnepym4NFIPbla6+9ZthsNmPcuHHGb7/95jyelZVlJCUlGTabzfjyyy9dbnMw8HU/nur77783LrjgAufvyZp+r37//feGzWYzhgwZYuzbt895fOvWrUZiYqIRHx9vbNiwweU2B4tA7Euee/wPQRbqtHjxYsNmsxkTJ06scu7IkSNG3759jXPOOcc4evRonWWtW7fOsNlsxqWXXmrY7fZK50pLS42LL77YsNlsxo4dO5zHHQ/Vjz76aJXydu7cadhsNmPw4MFGeXm5YRiGsXfvXsNmsxnnn3++UVBQUOWeDRs2OM9XVFTU2eZgEmh9ebr09HSjV69exnXXXdfog6xA7MuUlBTDZrMZH3/8cZV7srOzjcGDBxvXXXddne0NNoHYl46HxqVLl1a5Z+bMmYbNZjOmTZtWZ3uDia/70TBOPlA/9thjRo8ePYxevXo5r6vrwXzx4sVVzi1YsMCw2WzGnXfeWWd7g02g9SXPPf6JxBeo04oVKyRJI0aMqHIuKipKiYmJKisr06pVq1wua9iwYQoJqfzxCwsL09ChQyVJqampVe6pbm54XFycbDabcnNztXHjRklSXl6ezjvvPA0ePLjaYfGuXbtKkgoKCnTixIk62xxMAq0vT3X8+HHdd999ioqK0l/+8pc62xfsAq0vjx07pu+++05RUVEaPXp0lXtiYmK0evVqzZ8/v872BptA60tJzrIPHDhQ5Z7c3Fxn2xsTX/ejJL344ov6+OOPdfbZZ2v+/PlKTEyssY7jx4/rhx9+kNVq1bBhw6qcv/TSS2WxWPTNN9/IbrfX2eZgEmh9yXOPfyLIQp0yMzMlSfHx8dWe79atmyRp27ZtLpdls9lcLmvHjh1u3XPuuefqww8/1Msvv1zt9Y4HhZYtWza6ucmB1peneuqpp7R3714988wzLi0SDnaB1pdbt25VeXm5+vTpI6vVqq1bt2rWrFmaOnWqXnrpJW3durXOdgarQOtLSRoyZIgkadasWVqyZIkKCgqci+4/+eQTRUVFady4cXW2N5j4uh8l6eyzz9bTTz+tTz/9VP369au1jqysLNntdnXq1KnafwvbtGmjtm3bqri4WHv27KmzzcEk0PqS5x7/RHZB1OnQoUOSpDPOOKPa8x06dKh0nZll/fbbbyouLjatfrvd7lyMffnll9d5fbAJ1L787LPP9Omnn2rChAm66KKLdOzYsTrbF+wCrS+zs7MlSe3bt9ezzz6rd955R8YpeZdef/11/fnPf9aDDz5YZ3uDTaD1pSTdcMMN2r17txYtWqR777230vUXXHCBpk+fXutC/mDky350mDx5smuNdaEORz2HDx/WoUOHFBcX53LZgS7Q+rI2jf25x5cYyUKdCgsLJanG1J+O447rXCkrMjLSpbIc/23SpEmVYXZ36zcMQ0899ZQ2btyoNm3aaMqUKXW2N9gEYl/u3btX06ZNU7du3fTAAw/U2a7GItD6sqCgQJK0bNkyvffee5oyZYpWrlypNWvW6Omnn1bTpk315ptv6p///Ged7Q02gdaXkhQaGqqRI0eqe/fuioqK0kUXXaT+/fsrIiJC69at0/z581VRUVFne4OJL/vRE45pY7Wl9Q4PD693PYEo0PqyJjz3+BYjWaiT1WpVRUVFnXueGC7sBmC1WiXJ5bIc/+i7st9KbfWXl5fr8ccf17///W+Fh4dr1qxZzm+PGpNA68vy8nI98MADKikp0QsvvOD8Bx+B15clJSWSTq7Nuvfeeyt9Szt27Fg1a9ZM99xzj2bPnq0//elPatKkSZ1lB4tA60tJmjNnjl5++WWNGjVKH374oZo1aybp5JciU6ZM0TvvvCOLxaJHHnmkznKDhS/70ROu1lHfegJRoPVldXju8T1GslAnxz+eRUVF1Z53TDWp6Vua+pTluL6kpKTGb0Xrqv/YsWOaPHmy/v3vf6tp06aaO3euBgwYUGdbg1Gg9eXs2bO1fv163XvvverRo0edbWpMAq0vHf+1WCyaMGFCletHjRqlqKgoHTt2TJs3b66zzcEk0Ppyz549evXVV9WhQwfNmDHDWYYkdenSRS+++KJCQkL0/vvv68iRI3W2OVj4sh894ajDUVZ1HF+OuLvxbqALtL48Hc89/oEgC3VyfPPhyBh1Osc8Yle+IXG3rObNmzsXaXpSf3Z2tsaNG6fVq1erQ4cOeu+995SUlFRnO4NVIPXl4cOHNXfuXIWGhmrz5s26//77nX+eeOIJ5z2OY1lZWXW2OZgEUl9KUtu2bZ3/remBLTo6WpKUn59fZ5uDSaD1ZVpamux2u/r3719tX3bt2lUxMTEqKytrVJuF+7IfPeFYI1RTHWbVE4gCrS9PxXOP/yDIQp0c2XV27txZ7XnH8Zqy8NS3LEdGHnfr37hxo8aNG6fdu3erV69eWrhwoc4555w62xjMAqkvi4uLZbfbVV5ers8//1yfffaZ88+XX37pvMdx7PDhw3W2OZgEUl+e+t9TEy2cztGHjoCssQi0vvztt98k/W8aVHVCQ0+uRigvL6+zzcHC1/3orm7duslqteqXX36pdpTlyJEjysvLU3h4uGJjYz2uJxAFWl868NzjXwiyUCdHqt6lS5dWOZefn6+0tDSFhYVp8ODBLpe1fPnyKlNTysrKnPtEXHLJJS7Vn5WVpczMTLVp00bnnnuu8/j27dt100036ejRo7r44ov1/vvvN7pMV9UJpL6Mjo7W9u3bq/2zbt06532OY7XtIRKMAqkvpZMP8p07d1ZZWVmlINlh27ZtOnDggFq2bKmePXvW2eZgEmh96dhzZ82aNdUGzPv27dOePXtksVga1TRfX/eju8LDwzVo0CCVlZU593I61dKlS2UYhi688EKFhYV5XE8gCrS+lHju8UcEWajTiBEj1KlTJ61evVrvv/++83hxcbGmTp2qwsJCjRkzRu3atXOeKysrU1ZWlrKyslRWVuY83q9fPyUkJCgrK0szZ850LvS02+2aMWOG9u/fr4suuqjSQ9bo0aPVvHlzLVq0qNJmfUePHtWjjz4qSfrzn//s/EegtLRUd999t44dO6YLL7xQs2fPbnTzyWsSaH2JmgVaX1osFt18882SpOeff14ZGRnOe3JzczV16lQZhtHokl5IgdeXF110kbp06aLDhw/r0UcfrRRoHTx4UPfff7/Ky8t1xRVXqH379ia/W/7L1/3oieuvv17SyZ/JU/fC2rZtm3PPJbNSiQeSQOtLnnv8k8VobClj4JG0tDRNmjRJxcXFOueccxQdHa3169fr0KFD6tWrl+bPn19pg7t9+/Y5d5BPTU11rrWQTm58ed111+no0aPq2rWrunfvrq1btyonJ0edO3fWggULquwn8dlnn+nBBx+UYRg677zz1LZtW61bt05Hjx7V7373O7322mvOB4CPP/5Yjz/+uKSTDwOtWrWq8XU99dRTlRZtNwaB1Jc1OXbsmHMRb2Na83G6QOtLwzA0depULVq0SCEhIerfv7/Cw8OVnp6ugoICJSYm6o033mh0QZYUeH25bds2/fnPf1ZeXp5at26tfv36qbCwUBs3blRhYaHOPfdcvfXWW41u41Nf9+PpHn74YX3yySd68MEHddNNN1V7zZNPPqkPP/zQObJlt9uVlpamsrIy3XXXXbrttttMeGcCTyD1Jc89/okgCy7LzMzUq6++qh9++EGFhYWKjo7WyJEjddNNN1X5h7S2XzaS9Msvv+jVV1/VqlWr9Ntvv6ljx44aMmSIbrnllkrfDJ3qxx9/1Ny5c5Wenq7y8nJ16dJFo0ePrvLN96233lrt1IfqrFmzRm3atHHnbQgKgdKXNSHI+p9A7MuvvvpKH3zwgbZs2aLy8nLFxsbqqquu0nXXXedcy9MYBVpfHj58WG+++aZWrFihX3/9VVarVV27dtXvf/97jR8/vlEGy5Lv+/FUrgRZhmHo3//+txYsWKCdO3cqPDxc3bp104033qjhw4d7+C4Eh0DpS557/BNBFgAAAACYiDVZAAAAAGAigiwAAAAAMBFBFgAAAACYiCALAAAAAExEkAUAAAAAJiLIAgAAAAATEWQBAAAAgIkIsgAAAADARARZAAAAAGAigiwAAAAAMBFBFgAAAACYiCALAAAAAExEkAXAI/v27VN8fLzi4+OVlpZmevmGYSgrK8v0cs1WVFSkffv2+bQNDz/8sOLj43X//ff7tB319e9//9v5mTr9T+/evTVgwAD9/ve/1/Tp07Vly5ZayyorK1Pv3r1rLC8+Pl79+/dvoFdWM8drvuiii0wtd8eOHVWODR06VPHx8Vq4cKGpdfmSoy+///57XzfFY6f+Ls3OzvZ1cwCYJNTXDQCA023cuFF/+ctfFBsbq7///e++bk6NPvvsM/3tb3/THXfcobFjx/q6OUGlX79+lf6/vLxc+fn52rlzpzIzM7VgwQLdeOONevDBB6u9PysrS2VlZYqMjFTPnj2rvaZZs2amt9vXDh06pOeff14//vijvv32W183BwAaLYIsAH7ngw8+0MaNGxUbG+vrptTqxRdf1MGDB33djKC0YMGCao/n5+frH//4h95++229+eabCgkJqXYEb9u2bZKk/v3764033vBqW/3J6tWr9fnnn+uMM86ocu6f//ynysrK1KFDBx+0DAAaF6YLAgACRlRUlB566CHdfffdkqR58+Zpw4YNVa5zBFk2m60hm+fXYmJiFBcXpxYtWvi6KQAQ9AiyAAABZ9KkSc4A6tVXX61yniALAOBLBFkA/EZaWpri4+P1ySefSDq55ik+Pl4TJkyQ9L8F4oMHD9bBgwc1adIk9enTRwMHDtR9992nq6++WvHx8Xr66adrrGPOnDmKj4/XzTffXKnOP/zhDyotLdVLL72kYcOGKSEhQUOHDtVjjz1WZTH6K6+8ovj4eP3yyy+SpMcee0zx8fF65ZVXKl138OBBPf/88xo1apTOPfdcnXfeefrDH/6gV199VceOHavSNke5M2bM0MGDB/XQQw/pggsu0LnnnqtRo0bp1VdfVUFBQa3v4S+//KLHH39cQ4YMUe/evfW73/1ODz/8sPbs2VPt9eXl5Vq8eLFuueUW/e53v1NCQoLOO+88jRw5Uk888YR2795d5Z4JEyYoPj5e//3vf7Vt2zbddddduuCCC9S7d28NGzZMzzzzjI4cOVJrO+srJCTEuQ5uzZo1OnHiRKXzZgdZjuQiCxYs0EcffaSLL75YCQkJuvTSSyslfjl+/Lhmz56t5ORknXfeeerbt69+//vfa9asWdX2eW2Ki4v1/vvv68Ybb3S+v/369dMVV1yh5557rspU1fj4eD3yyCOSTn72HMkUHE5PfOFIujFo0CCVlZVV24b9+/erZ8+e1SZlWLdune68805deOGF6t27ty644ALddtttWrNmjVuv0+HYsWP6xz/+ofHjxysxMVHnnHOO+vfvr9GjR+uVV17Rb7/9Vuv9n376qf74xz/q3HPPVWJioiZPnqzvvvuuynWuJJpwvFf//ve/ncccvyvGjRunsrIyvfXWW7ryyit17rnnqn///po4caK+/vrrGtu3ZcsW3XfffRoyZIj69Omj3//+93r//fdlGEatr+uHH37QAw88oOHDh6tv377On+s77rij2vfa8Xvk73//u44cOaKnn35aQ4cOdfbRPffco+3bt9dY37Zt2/T4449r+PDhSkhI0IABAzRx4kR99dVX1V5fWlqqd955R1dffbXOP/989enTRyNHjtSzzz6rQ4cO1fragGBGkAXAb7Ro0UL9+vVT27ZtJUlt2rRRv379qjwol5aW6qabbtL333+vuLg4WSwWde7cWX/84x8lSUuWLFF5eXm1dfznP/+RJI0ePbrS8fLyck2aNEmvvfaaSkpK1L17d+Xl5WnhwoUaPXq01q5d67z2zDPPVL9+/dSkSRNJUmxsrPr166czzzzTec2aNWv0//7f/9Nbb72lnJwcnX322ercubMyMzP1yiuv6Morr6zxQefgwYMaM2aMFi9erBYtWig2Nla7d+/WK6+8oquvvloHDhyo9r7t27crOTlZCxcuVLNmzdSlSxfl5eXpk08+0VVXXaWdO3dWur64uFh//vOf9dBDD2nlypUKCwuTzWZTq1attGfPHn300UcaPXp0jZn8/vvf/2rMmDH6+uuvFRUVpTPPPFP79u3TO++8o2uuuUbHjx+v9j6znH/++ZJOZhJcv3698/jBgweVn58vq9WqZs2a6bXXXtMtt9yiG264QY899li9EkJ8+umneuKJJ2QYhs466yzl5uY6E2tkZWXpyiuv1KxZs5SZmakOHTooNjZWu3btcgZermbMPHLkiMaOHavp06drzZo1at68ueLj49W0aVPt2LFDb7/9tq666qpKn4V+/frprLPOkiSFhYWpX79+VRKInOqyyy5T06ZNlZ+fr1WrVlV7zWeffaaKigr179+/0hrJv//977ruuuu0dOlSlZaWymazKSQkRKmpqbrhhhv0t7/9zaXX6bBnzx5deeWVeuGFF5Senq42bdooPj5eVqtVmzdv1quvvqqrr766SjDtMGfOHD3wwAPavXu3unXrppCQEH3zzTf685//XO1IZ32UlZUpJSVFzz//vA4dOqS4uDjZ7XatXbtWt99+e7XrCT/99FONGzdOn3/+uYqKitS9e3fl5uZq+vTpevTRR2us64UXXtCECRP06aef6sSJE+ratas6deqkI0eOaNmyZbrhhhv00UcfVXvvr7/+quTkZL333nuSpLi4OOXn5+uLL77Q1Vdfrc2bN1e55/3339eYMWP08ccf68iRI+revbuaNm2qtWvX6q677tKLL75Y6fpDhw5p3LhxeuaZZ7Rhwwa1atVK3bp10/79+/XPf/5Tv//97/XTTz+58/YCwcMAAA/s3bvXsNlshs1mM9auXWtq2Q899JBhs9mM++67r8Y6Bw4caOzYscMwDMMoKSkxCgoKjIKCAuPcc881bDabsWLFiirl/vzzz4bNZjMGDBhglJSUGIZhGGvXrnWWGR8fb7z99tuG3W43DMMwjh49atx6662GzWYzkpKSjGPHjlUq75JLLjFsNpvx8ccfVzq+b98+o2/fvobNZjNuueUWIzc313kuJyfHuPrqqw2bzWZcfPHFlcqcNWuWsy0DBgwwvv/+e+e5nTt3Gpdeeqlhs9mMP//5z9W+XzabzUhOTjaysrKc57Zu3WokJiYaNpvNuOuuuyrd56gvMTHR2LBhQ6VzGzZsMAYPHmzYbDbjjjvuqHTuuuuuc9Y3adIk4+DBg85zX3/9tdGzZ0/DZrMZb7/9dpU+qM2iRYuc5bri6NGjzutP7YNvvvnGsNlsRq9evYyEhATnNaf+mTRpklFQUOBy2059j6dPn26Ul5cbhmEYeXl5hmEYxokTJ4wRI0YYNpvNuPXWW40DBw447z106JAxadIkw2azGZdeeqlRVFRU5TX/7ne/q7a+ESNGGLt376507r///a/zc/7cc89VOldTeYZR/ef1kUceMWw2m3H33XdX+7pHjRpl2Gw2Y+HChc5jCxYsMGw2m9G/f3/jP//5j/N4RUWFsWTJEudn//Sfi9o4PlPjxo2r9HmqqKgwPvnkE6NHjx6GzWYz3nvvvUr3ndqnjz76qHHixAnDMAyjvLzcmDNnjvPcqT9Lp/4e2bNnT7XtcbxXixYtch479XdF3759jU8//dR57tixY8bEiROdv5vKysqc53Jycpyfw2effdb5u6e8vNyYO3dupddwansc9fXo0cP417/+5fy9ZBiGsX//fud7dsEFF1Q6d+rvkZEjRxobN250nsvKyjIuuugi5+f0VD/99JPzff773/9uFBcXO88tWrTIeW7VqlXOvnH8Lrv22msr/d45duyY87OVmJhoHDp0qNr3GQhmjGQBCEh/+tOf1K1bN0lSkyZN1Lx5czVv3lwjR46U9L8Rq1M5piFeccUVzlGoU91444264YYbFBJy8ldjq1at9OKLLyo6Olp5eXk1Zrw73dy5c1VYWCibzaaXX35Z7dq1c57r0qWL5s6dq/bt2+vXX3/V/Pnzqy3j+eefV1JSkvP/4+LiNGfOHIWEhGj16tVKT0+vck9oaKhmz56trl27Oo/16NFDN9xwgyTpxx9/rHT9999/r5CQEE2ZMkV9+vSpdK5Pnz669tprJUmZmZnVtrFt27aaNWtWpWx1w4YNc+759PPPP1d7n1lOTcGen5/v/LtjhLC8vFyXXHKJFi1apI0bN+q7777TE088oWbNmumbb77RPffc43ad4eHhuu+++2S1WiWdHG2VpIULFyo7O1vnnHOOXnnllUrZ/dq3b6+XX35ZnTt31p49eypNQatOeXm5fvzxR1ksFj3yyCPO0SmH3/3udxo1apSkmvvGVY4R3RUrVlQZedy8ebN27typpk2b6vLLL5d0chTZMS32mWee0ZVXXum83mKxaNSoUXrggQcknZy2VtOI8qny8vKc+3r95S9/qfR5slgsSk5O1sCBAyWpxtHffv366emnn1bTpk0lSVarVbfeequzfXPnzq2zHe6488479fvf/975/y1atHC+7qNHj1aaZvvGG2+opKREAwcO1MMPP+z83WO1WjVp0qQqo+oOq1atUpMmTTRixAj98Y9/dP5ekqSOHTvqrrvukiQdPnxYeXl51ZbxwgsvKCEhwfn/Xbt2df4+OP3nc86cOaqoqNDll1+u++67T+Hh4c5zo0eP1pgxYyTJ+flNTU3V+vXr1aFDB73xxhuVfu+0aNFCM2bM0Lnnnqv8/Hz985//rLZ9QDAjyAIQkBxTxU7nmDK4YsWKSuuXSktL9eWXX0qqOlXQYeLEiVWOhYeH66qrrpJ08qHCFd98840k6dprr602mGvVqpWzndWt4YiNjdUll1xS5XhcXJwGDBhQY1t69+6tTp06VTnuWJdz9OjRSscXLFigjRs36pprrqn2dURGRko6Oa2wOklJSZUexE5tp6Q614/V16nriCwWi/Pv8fHxuvbaa3XHHXfo5ZdfVu/evRUeHq527dpp/Pjx+sc//iGLxaL//ve/bk8d7NWrl/NB/lSOfhw1apQzADtVRESE8wuAlStX1lpHaGiovv76a23YsEEXX3xxlfOGYTjbUFPfuKp///4666yzVFxcrOXLl1c6t3jxYknSyJEjnQHt+vXrdfjwYTVr1kzDhg2rtswrr7xSISEhOnjwYJ2bRksng/W1a9dqw4YN1a6hs9vtat68uaSaX+/48eMrfQYcxo0bJ+nk+rHCwsI62+Kqmn4+HU5df+f4jNX0e8fxZcbp7r//fm3cuLHGqZcRERHOv1f3vnTo0EHnnHNOleOOYOjUn8+ioiLnlOirr7662vruvvtuffXVV3r++ecl/e8zP3z48Gp/JiwWizPIreszDwQj9skCEJDat29f7fGBAwcqNjZW2dnZ+uqrr5zJEVJTU3Xs2DHZbDb17t27yn0dOnRQx44dqy2zR48eklRj8ohTHT9+3JmQoLp6HBwPP9Ulljj1m+fTxcfHKy0trdq2VLc3kiTnA1BZWZnKy8sVGvq/X/1hYWEqKCjQzz//rD179mjv3r3as2ePtm7dqsOHD0uSKioqqi23pvocD3+ujGLUx6kPia1atXL+fciQIRoyZEiN9/Xv318XXHCBvvvuO3399de1Xnu6mj53jhGlhQsX1hiMO97PXbt2uVRXeHi48vLylJ6erj179mjfvn3atWuXtm7d6kwCUVPfuGP06NGaOXOmPv30U+cXCuXl5VqyZIkkOY9Jco44lZWVafz48TWWabVaVVFRoV27dlUZJa1JRESE9u/frw0bNignJ0d79+5VVlaWtm7d6gyQanq9vXr1qva44wuG8vJyZWdn17gxtbuq++yfGvTY7XZJJ4Of/fv3S5K6d+9ebVk9evSQxWKpNgGGxWJRSEiIfvzxR+3cuVN79+5VTk6Otm/fXilpR3Xvizs/n7/++qvzSwvH77vTtW3b1rleVvrfZ37lypXORDOncwSbe/bskWEY1QbCQLAiyALQYLZs2aK//OUv1Z774x//6JyO4opTH2hOd9VVV+mll17Sf/7zH2eQ5fhW3jGCdLrWrVvXWJ4jSHFlZObUhfmOb9+r4zhXWFhY5eHDlbZUl6muulGz2hw/flwzZ87UJ598Uulb/rCwMJ1zzjnq2bNnjQkRHNe56i9/+UuNoxquTsM83alJJE6dquSKnj176rvvvtO+ffvcuq+6kTtJzql2e/bsqTMYd+VzlJubq+eff15fffVVpRG7yMhIJSQkyG63m5ZQIDk5WS+99JLWrl2rQ4cOqUOHDvruu++Ul5en6Oho51S9U9teWlrq0nRQVzMq7tq1S3/961/17bffVgoYmjdvrv79++vQoUM1PshLlaeO1nS8qKjIpba4oq7PviNgOjUjYnWjPdLJn9vIyMgqI22GYeidd97Rm2++WSlLn8Vi0dlnn60//OEP1U6LdrWNpzp1lLum9/J0js/8/v37nYFkTex2u06cOFHr70Qg2BBkAWgwjhGT6lxwwQWm1TN69GjNmjVLP/74o3799VdFRERo9erVCgsLq7SG5FS1TSVyPFg61t/U5tQHlNqy6zkevpo2bVrl293a2uIo89RvlD112223KS0tTREREbrxxht17rnnqnv37oqNjVVYWJg+/vjjWoMsd2RmZpq+RstRXnh4eJVpUXa7XYZhVBq1O5XjQb6m8+6KjIxUQUGBXn/99WqnkrmjpKREEydOVFZWllq3bq1rr71WvXv3VlxcnGJiYmS1WvXiiy+aFmSdccYZGjx4sFatWqUvvvhCN9xwg/Ph/aqrrqr0+XRMIT3nnHPqXFvmqry8PF133XXKy8tTp06dNG7cOPXq1Utdu3ZVdHS0LBaL7rvvvlqDrJp+Zmoa7XSobvSotvLcFRUV5fx7Tb8PDMNQaWlpleOzZ892rn8bNWqULrroInXr1k1du3ZVs2bNtGfP/2/v/mOirv84gD8P7kACAkrU3UFyE3PSbpZbgtlkISY4bFZOWzaNAIsfgquWg8g0A62mNSSNFYTGKIjrIixr0rUs2bBFzLA77k6E8eNA5ICAYQR8vn+w+3w5uEN+XN/86vOx+c95x953n8/d3q/36/16vRunDLJmYnwA2N/fP63fOuu98Nprr+GZZ55xyjiIbiUMsojofyY0NHTK81mcZfyksbKyEnfccQeGh4cRGRnpcPJgNpvR399vd6VVp9MBgNhoYypeXl5YsGABrl69irq6Oodbperq6gBgUlMD4L9bsuyZyVimUltbK57vlJeXh7CwsEnPcdQqfjYcNfiYrdHRUXGSv27dOnHCB4ydcdTW1oa9e/ciNjbW7uutWbW5fo5WSqUSFy9ehNFodBhkNTY2oq+vDwqFYspJbGVlJS5fvgypVIqSkhK794gzrw0wtjDx008/4dtvv8XWrVuh1WrFphPjKZVKAGPvZeLWUytBEFBdXY1FixZBLpffMMOqVqvR1dUFX19fqNVqu5/NxDPBJmpoaBDHNp71Oru7u+Oee+4BYBtY2wturl+/7rR6Qjc3NygUCrS2tkKn09n9PWhoaJi0tfbvv/9Gfn4+ACA5ORmpqamTXufMeyAwMBCurq4YGRmB0WhEaGjopOf8/vvvyM7ORlBQELKzs6FUKqHX66f8vTKbzejo6IBcLrdpaEJ0O2DjCyK66VhXzh2tMk+Hdevh2bNnxRoZR4XnwFjmw9p9cLzBwUFxtTgqKmpa47ROsj/99FO7k7je3l5x+6K1E994dXV1dlft9Xq9mL2xNlGYrfHb5OzVjg0ODoo1Odb6kpvJiRMn0NLSAhcXF+zatcvm/5YsWQJBEPDVV1/ZrQu7ePGiGGBau/TNlfWal5WV2W1CMDw8jKSkJGzZskVsHOCI9dp4enraDbCuXbsmNleZeG2sHehm+t2JjIyEr68vamtrUVpaisHBQYSGhiIgIMDmeQ8++CC8vb0xMDDgMJNVUVGBnTt3Ijo6elqBgPX9yuVyuwGWyWQSu2k6uhfVarXdx63B/dq1a8Xtc76+vuJ31159nFardWo94aOPPgoAKCkpsTt+6+HQ43V3d4vZNHvNKya+bq7j9fLyEpsJOfosT58+jZqaGrS0tEAikYj3/DfffOOwu2FGRga2bduGF198cU7jI/p/xCCLiOasr68PFotlyn8zmfRZt9y1tbXNekwRERHw8/PDr7/+ivPnz2P+/Pk3bHBw5MgRmw5rFosFqampaGtrg1KpnBSkWbfYtLa22jyekJAAT09PGAwGpKWl2UxAmpub8fzzz+PatWtYuHCh3Y6GgiAgNTXVZoVYr9cjJSUFgiDg8ccft+lkNhvja5jef/99m0mayWRCQkKCWFvkzFqWuWpvb0dWVpa4jSopKWlSM4OEhARIJBL88ccfyMzMtNmmVV1djaSkJAiCgMcee2zKJiMzsX37dvj7+6OpqQmJiYk2967FYsGePXtw+fJlyGQyPPfcc1P+Leu16e3txcmTJ22+O7W1tYiNjRVraCZem/E1ezM5DNrNzQ0xMTEQBAE5OTkAbBtejP/71qA2KysLarXapoaqsrISr7/+OgAgOjpazB5Nxfp+9Xo9vvvuO/FxQRBw7tw5xMfHi3Vpju7F77//HkePHhUXNYaGhvDWW29Bq9VCJpMhOTlZfO68efPERhnHjh2zyZL9/PPPeOONN2445pmIi4uDr68vLl26hPT0dPG6CIKA4uJinDp1atJr7rrrLrE2s7Cw0Ka2y2KxYP/+/Th9+rT42Fy7TAJj3yWJRILy8nJ88MEHNr8JX375pRiwxsfHAxhboLj33nvx559/Ii4uzub3qr+/H/v370dVVRUkEsmkhRCi2wG3CxLRnI2fwDjyyy+/4M4775zW37NOmmtqahAVFYXg4GDk5ubOaExubm7YtGkTTp06hZGREWzfvv2G9TeBgYFISUmBQqGAr68vjEYjhoaGIJfLkZOTM2nbU0hICAwGAz766COcO3cO69evR1JSEgIDA5GTk4O0tDRotVqEh4cjODgYIyMjMJlMGB0dhVwuR25urt2V+0WLFqG7uxubNm3C0qVLIQgCTCYTBEFAWFgYXn311Rl9FvaEhIQgOjoaZ86cQUFBATQaDRQKBXp6esTMwpo1a3D+/HkMDAw43Er5T5nY1vqvv/6CxWIRC+xdXV3xwgsvYPfu3ZNeu2rVKmRkZODw4cPQaDQ4c+YMlEol+vr6xPcWHh6ON99802nj9fHxwYkTJ5CYmIiqqiqsW7cOwcHBkEgkuHLlCoaGhiCVSnH06FGx450jEREReOCBB/Dbb78hOzsbH374IRYuXIjOzk50dHRAIpHgoYceQlVVFa5evWrTOGXZsmVwcXHB9evXERUVhQULFiA/P9+mNsiRJ554AkVFRRgYGICnp6fDbGlCQgKam5tRWlqKjIwMvPPOOwgICEBHR4fYoMF6btV0bNmyBcXFxWhqakJqaioUCgX8/PxgNpvR1dUFmUyGVatW4cKFCw63DW7YsAF5eXkoKSlBQEAAmpub0dvbC5lMhkOHDk0KxPfs2YPExESYTCZERkYiODgYvb29aG1thUqlwsqVK6d9ZMON+Pv747333kNKSgrKy8tx9uxZLFmyBO3t7ejs7ERERAR+/PFHmyyXVCpFWloaDhw4gAsXLiA8PBxBQUEYGhpCU1MThoeHERISArPZjO7ubrS3tzvMeE3X6tWrkZ6ejsOHD+Pdd99FQUEBAgMD0d7eLnbGTE5OFherZDIZjh8/jvj4eOh0OsTExECpVMLDwwONjY1iJi49Pd1uxp7oVsdMFhHddDZv3oz4+Hj4+/ujpaUFOp1uVq2qx2eeptoqaPXJJ58gNjZWDIYUCgUSExPxxRdf2D2/Z+/evdiwYQM8PDzQ0NBg0+3u4Ycfxtdff41nn30WAQEBuHLlCsxmM5YvX46XXnoJ5eXlDidFcrkcn3/+OaKiotDR0YG2tjaoVCocOHAA+fn58Pb2nvFnYc+RI0dw8OBBqFQqjI6Oor6+HkNDQ3jkkUeQl5eHgoICKBQKAGNbqP6XampqbP7V19djcHAQKpUKcXFxqKiosFunYrVjxw589tlniImJgY+PD0wmE/r7+xEWFoa3334beXl5DjsFzpZKpUJFRQWSk5OxbNkyseX6/PnzsXnzZqjVanHr2FRcXV1RWFiIl19+GcuXL8fg4CAMBgOkUik2btyIoqIiHD9+HO7u7ujp6bFpKLJ48WIcOnQIQUFB6OnpgdlsnpRpdeS+++4T23dHR0fb1LmNJ5FIcPDgQeTn52P9+vWQSqXQ6XQYGBjA/fffj8zMTJw8edJhN72JvLy8UFZWhl27dmHp0qWwWCwwGo3w8vLCk08+CbVajezsbABj2S57Ge7MzEzs27cP/v7+MBgMcHV1xcaNG1FWVmZzaLDV2rVrUVxcLJ7xZDKZ4O7ujtTUVBQXF0977NO1evVqaDQabNu2DX5+fqivr4eHhwd2794tZg4nevrpp1FYWIg1a9bA29sbRqMRXV1dWLFiBfbt24fS0lIx4HHWOVQ7d+5ESUkJYmJi4O7ujvr6egwPDyM8PBwff/zxpO9cYGAgNBoNXnnlFaxYsQKdnZ0wGAxikF5UVGQ3W090O5AIcyl6ICK6iWm1WiQmJkKlUqGsrMzuc6qrq7Fjxw4AwKVLl5zWbW42jh07htzcXKxcuXLWbc2JiIjo38dMFhHdskpLSwEAW7du/ZdHQkRERLcT1mQR0S1jZGQEer0ePj4+0Gg0+OGHH3D33Xc7PBuLiIiI6J/AIIuIbhkuLi546qmnbNqmp6enY968ef/iqIiIiOh2w+2CRHTLkEgkCA0NhYeHBxYvXoysrCy7Re9ERERE/yQ2viAiIiIiInIiZrKIiIiIiIiciEEWERERERGREzHIIiIiIiIiciIGWURERERERE7EIIuIiIiIiMiJGGQRERERERE5EYMsIiIiIiIiJ2KQRURERERE5EQMsoiIiIiIiJyIQRYREREREZETMcgiIiIiIiJyIgZZRERERERETsQgi4iIiIiIyIn+AyhApIH2rbf6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(metab_data_rel_v9.loc[meta_v9.index, 'L-tryptophan-D5'], meta_v9['median_mmNorm'])\n",
    "_ = plt.xlabel('L-tryptophan-D5 relative abundance')\n",
    "_ = plt.ylabel('Median normalized titer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "683c0dc5-ba35-4772-97a4-3d095e746a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(metab_data_clr_v9.loc[meta_v9.index, 'L-tryptophan-D5'], meta_v9['median_mmNorm'])\n",
    "_ = plt.xlabel('L-tryptophan-D5 CLR abundance')\n",
    "_ = plt.ylabel('Median normalized titer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2244404d-3a89-4024-b67d-f4e48c1e892a",
   "metadata": {},
   "source": [
    "It looks pretty correlated no matter what normalization you use. This make me hesitant to use the 1 year time point metabolomics data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c69a87e-e51f-4eaf-92a9-c1d9c06f53e8",
   "metadata": {},
   "source": [
    "## Correlate un-normalized with separate median titers\n",
    "\n",
    "Now that we have looked at the combined median titers with both time points we will split into the DTAPHib and PCV medians separately."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326cc2ad-9a03-4603-9ba2-07112b10ab39",
   "metadata": {},
   "source": [
    "### 2 month DTap hib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "be5156a8-fbff-4645-a5af-a0b489c45c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.563852</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>0.037958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.515191</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.072309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>0.456488</td>\n",
       "      <td>0.005135</td>\n",
       "      <td>0.189994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <td>0.435376</td>\n",
       "      <td>0.007956</td>\n",
       "      <td>0.191652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Pyridoxic acid</th>\n",
       "      <td>0.420958</td>\n",
       "      <td>0.010569</td>\n",
       "      <td>0.191652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rho   p_value     p_adj\n",
       "Pyruvic acid            -0.563852  0.000342  0.037958\n",
       "Phenylpyruvic acid      -0.515191  0.001303  0.072309\n",
       "Orotic acid              0.456488  0.005135  0.189994\n",
       "Pyridoxal hydrochloride  0.435376  0.007956  0.191652\n",
       "4-Pyridoxic acid         0.420958  0.010569  0.191652"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_DTAPHib_correlations = metab_data_v5.apply(spearmanr, b=meta_v5['median_mmNorm_DTAPHib']).transpose()\n",
    "v5_DTAPHib_correlations.columns = ['rho', 'p_value']\n",
    "v5_DTAPHib_correlations['p_adj'] = p_adjust(v5_DTAPHib_correlations['p_value'])\n",
    "v5_DTAPHib_correlations = v5_DTAPHib_correlations.sort_values('p_value')\n",
    "v5_DTAPHib_correlations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ed8ca3-c27c-4738-af6f-04b84dcfe111",
   "metadata": {},
   "source": [
    "Three things are significant after FDR correction when correlating 2 month titers with DTAPHib titers. Phenylpyruvic acid still and additionally pyruvic acid and 1-Methyl-1-butanol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "552d8ee0-4b06-4521-a2ac-72ccd1d38a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metab_data_v5_w_titer = metab_data_v5.copy()\n",
    "metab_data_v5_w_titer['median_mmNorm_DTAPHib'] = meta_v5.loc[metab_data_v5_w_titer.index, 'median_mmNorm_DTAPHib']\n",
    "_ = sns.scatterplot(x='Pyruvic acid', y='median_mmNorm_DTAPHib', data=metab_data_v5_w_titer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca1d702-fca7-4e49-895b-d80b00aeb1e0",
   "metadata": {},
   "source": [
    "Very similar curve to what we saw with phenylpyruvic acid with the median of all titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bf0af138-fc0c-4ee2-ac8d-6935fb8b7fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm_DTAPHib', data=metab_data_v5_w_titer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cde25d6-76ee-42dd-96f8-c997336b4000",
   "metadata": {},
   "source": [
    "Again very similar to the other significant curves. This one might be a bit more extreme/scattered. If you have < 2000 Phenylpyruvic acid acid then you are very likely to have high titers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c2c9d6-1444-46b7-bfa1-5940d64e30ff",
   "metadata": {},
   "source": [
    "### 2 month PCV\n",
    "\n",
    "Same idea as above but with the PCV titers instead of the DTapHib titers. There are two babies who have a median titer but do not have a median PCV titer because of a lack of measured PCV titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3f75fee5-8a63-42b0-b0ed-aacbbc22d3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitDate</th>\n",
       "      <th>PT</th>\n",
       "      <th>Dip</th>\n",
       "      <th>FHA</th>\n",
       "      <th>PRN</th>\n",
       "      <th>TET</th>\n",
       "      <th>PRP (Hib)</th>\n",
       "      <th>PCV ST1</th>\n",
       "      <th>...</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>protectNorm_Dip</th>\n",
       "      <th>protectNorm_TET</th>\n",
       "      <th>protectNorm_PRP (Hib)</th>\n",
       "      <th>protectNorm_PT</th>\n",
       "      <th>protectNorm_PRN</th>\n",
       "      <th>protectNorm_FHA</th>\n",
       "      <th>geommean_protectNorm</th>\n",
       "      <th>VR_group_v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P125_V12_08212020</th>\n",
       "      <td>125</td>\n",
       "      <td>V12</td>\n",
       "      <td>08212020</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.88</td>\n",
       "      <td>159.800978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037553</td>\n",
       "      <td>0.147901</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.533333</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.692432</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P136_V5_12062019</th>\n",
       "      <td>136</td>\n",
       "      <td>V5</td>\n",
       "      <td>12062019</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.34</td>\n",
       "      <td>254.805233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120167</td>\n",
       "      <td>0.024566</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>8.933333</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.918328</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P243_V5_09062018</th>\n",
       "      <td>243</td>\n",
       "      <td>V5</td>\n",
       "      <td>09062018</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.15</td>\n",
       "      <td>112.372819</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056522</td>\n",
       "      <td>0.146771</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.625</td>\n",
       "      <td>1.340035</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P233_V9_05232019</th>\n",
       "      <td>233</td>\n",
       "      <td>V9</td>\n",
       "      <td>05232019</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>14.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.51</td>\n",
       "      <td>1.38</td>\n",
       "      <td>344.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165297</td>\n",
       "      <td>0.419887</td>\n",
       "      <td>9.4</td>\n",
       "      <td>35.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1.125</td>\n",
       "      <td>5.375</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5.63809</td>\n",
       "      <td>HVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P204_V12_02112020</th>\n",
       "      <td>204</td>\n",
       "      <td>V12</td>\n",
       "      <td>02112020</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>399.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.37198</td>\n",
       "      <td>0.19273</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.625</td>\n",
       "      <td>5.125</td>\n",
       "      <td>1.414559</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BabyN VisitCode VisitDate    PT   Dip   FHA   PRN    TET  \\\n",
       "P125_V12_08212020    125       V12  08212020   2.5  0.05   8.0  16.0    0.6   \n",
       "P136_V5_12062019     136        V5  12062019   2.5  0.05   5.0   2.5   0.22   \n",
       "P243_V5_09062018     243        V5  09062018  10.0  0.61   5.0  18.0  0.054   \n",
       "P233_V9_05232019     233        V9  05232019   9.0  0.94  14.0  43.0   3.51   \n",
       "P204_V12_02112020    204       V12  02112020   2.5   0.4  41.0   5.0    NaN   \n",
       "\n",
       "                  PRP (Hib)     PCV ST1  ... median_mmNorm_PCV  \\\n",
       "P125_V12_08212020      1.88  159.800978  ...          0.037553   \n",
       "P136_V5_12062019       1.34  254.805233  ...          0.120167   \n",
       "P243_V5_09062018       0.15  112.372819  ...          0.056522   \n",
       "P233_V9_05232019       1.38       344.0  ...          0.165297   \n",
       "P204_V12_02112020       NaN       399.0  ...           0.37198   \n",
       "\n",
       "                  median_mmNorm_DTAPHib protectNorm_Dip protectNorm_TET  \\\n",
       "P125_V12_08212020              0.147901             0.5             6.0   \n",
       "P136_V5_12062019               0.024566             0.5             2.2   \n",
       "P243_V5_09062018               0.146771             6.1            0.54   \n",
       "P233_V9_05232019               0.419887             9.4            35.1   \n",
       "P204_V12_02112020               0.19273             4.0             NaN   \n",
       "\n",
       "                  protectNorm_PRP (Hib) protectNorm_PT protectNorm_PRN  \\\n",
       "P125_V12_08212020             12.533333         0.3125             2.0   \n",
       "P136_V5_12062019               8.933333         0.3125          0.3125   \n",
       "P243_V5_09062018                    1.0           1.25            2.25   \n",
       "P233_V9_05232019                    9.2          1.125           5.375   \n",
       "P204_V12_02112020                   NaN         0.3125           0.625   \n",
       "\n",
       "                  protectNorm_FHA geommean_protectNorm VR_group_v2  \n",
       "P125_V12_08212020             1.0             1.692432         NVR  \n",
       "P136_V5_12062019            0.625             0.918328         LVR  \n",
       "P243_V5_09062018            0.625             1.340035         NVR  \n",
       "P233_V9_05232019             1.75              5.63809         HVR  \n",
       "P204_V12_02112020           5.125             1.414559         LVR  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_PCV = meta_matched.loc[~pd.isna(meta_matched['median_mmNorm_PCV'])]\n",
    "meta_PCV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "73ad9121-4bfb-4ed4-928a-99805446b9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.670455</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.002180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deoxycytidine 5-triphosphate</th>\n",
       "      <td>0.542112</td>\n",
       "      <td>0.001119</td>\n",
       "      <td>0.062095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.412099</td>\n",
       "      <td>0.017171</td>\n",
       "      <td>0.513376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.407754</td>\n",
       "      <td>0.018500</td>\n",
       "      <td>0.513376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.382687</td>\n",
       "      <td>0.027946</td>\n",
       "      <td>0.519205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "Phenylpyruvic acid           -0.670455  0.000020  0.002180\n",
       "Deoxycytidine 5-triphosphate  0.542112  0.001119  0.062095\n",
       "xanthurenic acid             -0.412099  0.017171  0.513376\n",
       "Pyruvic acid                 -0.407754  0.018500  0.513376\n",
       "L-Serine                     -0.382687  0.027946  0.519205"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_PCV_v5 = meta_PCV.query(\"VisitCode == 'V5'\")\n",
    "metab_data_PCV_v5 = metab_data.loc[meta_PCV_v5.index].transpose()\n",
    "metab_data_PCV_v5 = metab_data_PCV_v5.loc[(metab_data_PCV_v5 > 0).sum(axis=1) > metab_data_PCV_v5.shape[1]*.2]\n",
    "v5_PCV_correlations = metab_data_PCV_v5.transpose().apply(spearmanr, b=meta_PCV_v5['median_mmNorm_PCV']).transpose()\n",
    "v5_PCV_correlations.columns = ['rho', 'p_value']\n",
    "v5_PCV_correlations['p_adj'] = p_adjust(v5_PCV_correlations['p_value'])\n",
    "v5_PCV_correlations = v5_PCV_correlations.sort_values('p_value')\n",
    "v5_PCV_correlations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1fced1-6d9f-47ea-bc7f-d0424062877b",
   "metadata": {},
   "source": [
    "Only one significantly correlated with PCV and it's our friend Phenylpyruvic acid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "24cd62d9-cd39-48cd-bdd3-2aa157492823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metab_data_PCV_v5_w_titer = metab_data_PCV_v5.copy().transpose()\n",
    "metab_data_PCV_v5_w_titer['median_mmNorm_PCV'] = meta_v5.loc[metab_data_PCV_v5_w_titer.index, 'median_mmNorm_PCV']\n",
    "# metab_data_PCV_v5_w_titer\n",
    "_ = sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm_PCV', data=metab_data_PCV_v5_w_titer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bc3e0d-e12c-47ed-8b38-4ed6e8115765",
   "metadata": {},
   "source": [
    "Different compound same curve."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15447c4-ecd4-4da1-92cc-a84fa311cc98",
   "metadata": {},
   "source": [
    "### 12 month DTAPHib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "93a5a51b-559b-4536-b7e7-0cbd9db55912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.553895</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.551589</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.545952</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>0.545012</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Isoleucine</th>\n",
       "      <td>0.527770</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.001118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine</th>\n",
       "      <td>0.524023</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.001118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthine</th>\n",
       "      <td>0.522634</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.001118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.498377</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.002252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.494886</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.002252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Histidine</th>\n",
       "      <td>0.493263</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.002252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>0.483430</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.002855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Kynurenine</th>\n",
       "      <td>0.480185</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.002903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>0.477878</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0.002903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Citrulline</th>\n",
       "      <td>0.472754</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.003185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.468227</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.003257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rho   p_value     p_adj\n",
       "Guanosine               -0.553895  0.000020  0.000817\n",
       "serotonin                0.551589  0.000023  0.000817\n",
       "L-Serine                 0.545952  0.000028  0.000817\n",
       "hydroxyphenyllactate     0.545012  0.000029  0.000817\n",
       "L-Isoleucine             0.527770  0.000058  0.001118\n",
       "Creatinine               0.524023  0.000067  0.001118\n",
       "Xanthine                 0.522634  0.000071  0.001118\n",
       "L-Phenylalanine          0.498377  0.000170  0.002252\n",
       "L-Leucine                0.494886  0.000192  0.002252\n",
       "L-Histidine              0.493263  0.000203  0.002252\n",
       "xanthurenic acid         0.483430  0.000283  0.002855\n",
       "L-Kynurenine             0.480185  0.000315  0.002903\n",
       "N-Acetylneuraminic acid  0.477878  0.000340  0.002903\n",
       "L-Citrulline             0.472754  0.000402  0.003185\n",
       "5-HIAA                   0.468227  0.000464  0.003257"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_DTAPHib_correlations = metab_data_v9.apply(spearmanr, b=meta_v9['median_mmNorm_DTAPHib']).transpose()\n",
    "v9_DTAPHib_correlations.columns = ['rho', 'p_value']\n",
    "v9_DTAPHib_correlations['p_adj'] = p_adjust(v9_DTAPHib_correlations['p_value'])\n",
    "v9_DTAPHib_correlations = v9_DTAPHib_correlations.sort_values('p_value')\n",
    "v9_DTAPHib_correlations.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c7d1d5-970b-415c-8d74-c8291c34bc7f",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2089ea63-41ef-40c4-a7ac-2ffe924085ef",
   "metadata": {},
   "source": [
    "### 12 month PCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "26b400e9-1a3b-42eb-9be8-52d92ba6231b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Xanthine</th>\n",
       "      <td>0.449231</td>\n",
       "      <td>0.000944</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.447873</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.441991</td>\n",
       "      <td>0.001165</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>0.430860</td>\n",
       "      <td>0.001598</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Isoleucine</th>\n",
       "      <td>0.430372</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine</th>\n",
       "      <td>0.423549</td>\n",
       "      <td>0.001955</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.421810</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riboflavin</th>\n",
       "      <td>0.421196</td>\n",
       "      <td>0.002084</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.415847</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <td>0.410769</td>\n",
       "      <td>0.002752</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthosine</th>\n",
       "      <td>0.408778</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Kynurenine</th>\n",
       "      <td>0.405882</td>\n",
       "      <td>0.003126</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.403077</td>\n",
       "      <td>0.003360</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.402081</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>0.401502</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>0.025887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        rho   p_value     p_adj\n",
       "Xanthine           0.449231  0.000944  0.025887\n",
       "serotonin          0.447873  0.000982  0.025887\n",
       "L-Phenylalanine    0.441991  0.001165  0.025887\n",
       "L-Proline          0.430860  0.001598  0.025887\n",
       "L-Isoleucine       0.430372  0.001620  0.025887\n",
       "Creatinine         0.423549  0.001955  0.025887\n",
       "L-Serine           0.421810  0.002050  0.025887\n",
       "Riboflavin         0.421196  0.002084  0.025887\n",
       "L-Leucine          0.415847  0.002406  0.025887\n",
       "Phenaceturic acid  0.410769  0.002752  0.025887\n",
       "Xanthosine         0.408778  0.002899  0.025887\n",
       "L-Kynurenine       0.405882  0.003126  0.025887\n",
       "Guanosine         -0.403077  0.003360  0.025887\n",
       "5-HIAA             0.402081  0.003447  0.025887\n",
       "Orotic acid        0.401502  0.003498  0.025887"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_PCV_v9 = meta_PCV.query(\"VisitCode == 'V9'\")\n",
    "metab_data_PCV_v9 = metab_data.loc[meta_PCV_v9.index].transpose()\n",
    "metab_data_PCV_v9 = metab_data_PCV_v9.loc[(metab_data_PCV_v9 > 0).sum(axis=1) > metab_data_PCV_v9.shape[1]*.2]\n",
    "v9_PCV_correlations = metab_data_PCV_v9.transpose().apply(spearmanr, b=meta_PCV_v9['median_mmNorm_PCV']).transpose()\n",
    "v9_PCV_correlations.columns = ['rho', 'p_value']\n",
    "v9_PCV_correlations['p_adj'] = p_adjust(v9_PCV_correlations['p_value'])\n",
    "v9_PCV_correlations = v9_PCV_correlations.sort_values('p_value')\n",
    "v9_PCV_correlations.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb41fe42-a959-4fd7-9758-b18085c00c75",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616f4d66-23f6-463a-807a-9a07c6969ac5",
   "metadata": {},
   "source": [
    "## Correlate relative abundance with separate median titers\n",
    "\n",
    "Now we will do the same thing but with relative abundance for PCV and DTAPHib."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c30a3401-77ae-4524-9dd5-25dad23b2f1b",
   "metadata": {},
   "source": [
    "### 2 month DTAPHib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a78de7cb-fb53-4a13-94c2-78a282e5f7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.557415</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.045845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.523944</td>\n",
       "      <td>0.001039</td>\n",
       "      <td>0.057680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <td>0.470906</td>\n",
       "      <td>0.003747</td>\n",
       "      <td>0.138651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Pyridoxic acid</th>\n",
       "      <td>0.439238</td>\n",
       "      <td>0.007359</td>\n",
       "      <td>0.176632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>0.435376</td>\n",
       "      <td>0.007956</td>\n",
       "      <td>0.176632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rho   p_value     p_adj\n",
       "Pyruvic acid            -0.557415  0.000413  0.045845\n",
       "Phenylpyruvic acid      -0.523944  0.001039  0.057680\n",
       "Pyridoxal hydrochloride  0.470906  0.003747  0.138651\n",
       "4-Pyridoxic acid         0.439238  0.007359  0.176632\n",
       "Orotic acid              0.435376  0.007956  0.176632"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_DTAPHib_correlations_rel = metab_data_rel_v5.apply(spearmanr, b=meta_v5['median_mmNorm_DTAPHib']).transpose()\n",
    "v5_DTAPHib_correlations_rel.columns = ['rho', 'p_value']\n",
    "v5_DTAPHib_correlations_rel['p_adj'] = p_adjust(v5_DTAPHib_correlations_rel['p_value'])\n",
    "v5_DTAPHib_correlations_rel = v5_DTAPHib_correlations_rel.sort_values('p_value')\n",
    "v5_DTAPHib_correlations_rel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d0ce93-b31d-48f4-8faa-0bd7c5ee0032",
   "metadata": {},
   "source": [
    "Same three compounds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883f99a3-58b9-459b-9994-8cf752d3565d",
   "metadata": {},
   "source": [
    "### 2 month PCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fb1a01f1-080f-43ac-9c40-f62eae38f63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.627340</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.010361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deoxycytidine 5-triphosphate</th>\n",
       "      <td>0.500334</td>\n",
       "      <td>0.003025</td>\n",
       "      <td>0.167868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>0.408422</td>\n",
       "      <td>0.018290</td>\n",
       "      <td>0.462587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Mannose</th>\n",
       "      <td>0.372660</td>\n",
       "      <td>0.032693</td>\n",
       "      <td>0.462587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.372660</td>\n",
       "      <td>0.032693</td>\n",
       "      <td>0.462587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "Phenylpyruvic acid           -0.627340  0.000093  0.010361\n",
       "Deoxycytidine 5-triphosphate  0.500334  0.003025  0.167868\n",
       "myo-Inositol                  0.408422  0.018290  0.462587\n",
       "D-Mannose                     0.372660  0.032693  0.462587\n",
       "L-Serine                     -0.372660  0.032693  0.462587"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_rel_v5 = metab_data_rel.loc[meta_PCV_v5.index].transpose()\n",
    "v5_PCV_correlations_rel = metab_data_PCV_rel_v5.transpose().apply(spearmanr, b=meta_PCV_v5['median_mmNorm_PCV']).transpose()\n",
    "v5_PCV_correlations_rel.columns = ['rho', 'p_value']\n",
    "v5_PCV_correlations_rel['p_adj'] = p_adjust(v5_PCV_correlations_rel['p_value'])\n",
    "v5_PCV_correlations_rel = v5_PCV_correlations_rel.sort_values('p_value')\n",
    "v5_PCV_correlations_rel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e1bb31-3653-4679-a5ae-7dc9fdd6ee0c",
   "metadata": {},
   "source": [
    "Just phenylpyruvic acid."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c929d6ef-4122-410f-96c0-2a3e05173545",
   "metadata": {},
   "source": [
    "### 12 month DTAPHib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3622ef79-bdef-4be1-a147-6815e10aab65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.594124</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.552870</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.001185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.527417</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.001570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.521609</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.001570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Mannose</th>\n",
       "      <td>-0.517680</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.001570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Sorbose</th>\n",
       "      <td>-0.517680</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.001570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.500085</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.002538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Kynurenine</th>\n",
       "      <td>0.494448</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.002654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.491544</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.002654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthine</th>\n",
       "      <td>0.468312</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.005140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.459088</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.006243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-3-Dihydroxyisovalerate</th>\n",
       "      <td>-0.452340</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>0.007037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Isoleucine</th>\n",
       "      <td>0.448838</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>0.007220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-pantothenic acid</th>\n",
       "      <td>0.436027</td>\n",
       "      <td>0.001233</td>\n",
       "      <td>0.009775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxycytidine 5-diphosphate</th>\n",
       "      <td>0.430731</td>\n",
       "      <td>0.001435</td>\n",
       "      <td>0.009905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.430048</td>\n",
       "      <td>0.001463</td>\n",
       "      <td>0.009905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>0.428767</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.009905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.422019</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.010702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>0.422019</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.010702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>-0.406901</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>0.015302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>0.404254</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>0.015626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Histidine</th>\n",
       "      <td>0.395285</td>\n",
       "      <td>0.003728</td>\n",
       "      <td>0.018811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Xylose</th>\n",
       "      <td>-0.392210</td>\n",
       "      <td>0.004031</td>\n",
       "      <td>0.019454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>0.382644</td>\n",
       "      <td>0.005116</td>\n",
       "      <td>0.023662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mevalonic acid</th>\n",
       "      <td>0.380253</td>\n",
       "      <td>0.005425</td>\n",
       "      <td>0.024085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    rho   p_value     p_adj\n",
       "Guanosine                     -0.594124  0.000003  0.000381\n",
       "serotonin                      0.552870  0.000021  0.001185\n",
       "L-tryptophan-D5               -0.527417  0.000059  0.001570\n",
       "Inosine                       -0.521609  0.000073  0.001570\n",
       "D-Mannose                     -0.517680  0.000085  0.001570\n",
       "L-Sorbose                     -0.517680  0.000085  0.001570\n",
       "Adenosine                     -0.500085  0.000160  0.002538\n",
       "L-Kynurenine                   0.494448  0.000195  0.002654\n",
       "L-Serine                       0.491544  0.000215  0.002654\n",
       "Xanthine                       0.468312  0.000463  0.005140\n",
       "L-Phenylalanine                0.459088  0.000619  0.006243\n",
       "2-3-Dihydroxyisovalerate      -0.452340  0.000761  0.007037\n",
       "L-Isoleucine                   0.448838  0.000846  0.007220\n",
       "D-pantothenic acid             0.436027  0.001233  0.009775\n",
       "2-Deoxycytidine 5-diphosphate  0.430731  0.001435  0.009905\n",
       "L-Leucine                      0.430048  0.001463  0.009905\n",
       "xanthurenic acid               0.428767  0.001517  0.009905\n",
       "5-HIAA                         0.422019  0.001832  0.010702\n",
       "N-Acetylneuraminic acid        0.422019  0.001832  0.010702\n",
       "myo-Inositol                  -0.406901  0.002757  0.015302\n",
       "hydroxyphenyllactate           0.404254  0.002956  0.015626\n",
       "L-Histidine                    0.395285  0.003728  0.018811\n",
       "D-Xylose                      -0.392210  0.004031  0.019454\n",
       "L-Proline                      0.382644  0.005116  0.023662\n",
       "Mevalonic acid                 0.380253  0.005425  0.024085"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_DTAPHib_correlations_rel = metab_data_rel_v9.apply(spearmanr, b=meta_v9['median_mmNorm_DTAPHib']).transpose()\n",
    "v9_DTAPHib_correlations_rel.columns = ['rho', 'p_value']\n",
    "v9_DTAPHib_correlations_rel['p_adj'] = p_adjust(v9_DTAPHib_correlations_rel['p_value'])\n",
    "v9_DTAPHib_correlations_rel = v9_DTAPHib_correlations_rel.sort_values('p_value')\n",
    "v9_DTAPHib_correlations_rel.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b36a48-0582-497e-aaaf-b3dbb8a0b9f2",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1679ceae-f15f-4216-881c-693ccb28fdb9",
   "metadata": {},
   "source": [
    "### 12 month PCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2136e5fa-46c7-448f-811f-c26325bab273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-Sorbose</th>\n",
       "      <td>-0.485882</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.016703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Mannose</th>\n",
       "      <td>-0.485882</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.016703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.465068</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>0.021644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.443258</td>\n",
       "      <td>0.001124</td>\n",
       "      <td>0.031180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.423710</td>\n",
       "      <td>0.001947</td>\n",
       "      <td>0.040099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>-0.410679</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.040099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.402805</td>\n",
       "      <td>0.003384</td>\n",
       "      <td>0.040099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Isoleucine</th>\n",
       "      <td>0.401357</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.040099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>0.400452</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>0.040099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riboflavin</th>\n",
       "      <td>0.396833</td>\n",
       "      <td>0.003938</td>\n",
       "      <td>0.040099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.396471</td>\n",
       "      <td>0.003974</td>\n",
       "      <td>0.040099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.383620</td>\n",
       "      <td>0.005455</td>\n",
       "      <td>0.047088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.383167</td>\n",
       "      <td>0.005515</td>\n",
       "      <td>0.047088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      rho   p_value     p_adj\n",
       "L-Sorbose       -0.485882  0.000301  0.016703\n",
       "D-Mannose       -0.485882  0.000301  0.016703\n",
       "Adenosine       -0.465068  0.000585  0.021644\n",
       "Guanosine       -0.443258  0.001124  0.031180\n",
       "serotonin        0.423710  0.001947  0.040099\n",
       "myo-Inositol    -0.410679  0.002759  0.040099\n",
       "L-Phenylalanine  0.402805  0.003384  0.040099\n",
       "L-Isoleucine     0.401357  0.003511  0.040099\n",
       "L-Proline        0.400452  0.003593  0.040099\n",
       "Riboflavin       0.396833  0.003938  0.040099\n",
       "L-Serine         0.396471  0.003974  0.040099\n",
       "L-Leucine        0.383620  0.005455  0.047088\n",
       "L-tryptophan-D5 -0.383167  0.005515  0.047088"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_rel_v9 = metab_data_rel.loc[meta_PCV_v9.index].transpose()\n",
    "v9_PCV_correlations_rel = metab_data_PCV_rel_v9.transpose().apply(spearmanr, b=meta_PCV_v9['median_mmNorm_PCV']).transpose()\n",
    "v9_PCV_correlations_rel.columns = ['rho', 'p_value']\n",
    "v9_PCV_correlations_rel['p_adj'] = p_adjust(v9_PCV_correlations_rel['p_value'])\n",
    "v9_PCV_correlations_rel = v9_PCV_correlations_rel.sort_values('p_value')\n",
    "v9_PCV_correlations_rel.head(13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310c778e-fe9f-4346-abca-3b8da4f1c981",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ec7cfd-336f-4b8e-9e58-0f3f7ca9553a",
   "metadata": {},
   "source": [
    "## Correlate CLR with separate median titers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40408565-e8a1-4c6d-9f50-ac79a74835e5",
   "metadata": {},
   "source": [
    "### 2 month correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "756ab0fb-275f-41f3-8bd8-392824e76202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <td>0.550721</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.039113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.532698</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.039113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.517765</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.039113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>0.510041</td>\n",
       "      <td>0.001484</td>\n",
       "      <td>0.039113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Pyridoxic acid</th>\n",
       "      <td>0.499743</td>\n",
       "      <td>0.001914</td>\n",
       "      <td>0.039113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatine</th>\n",
       "      <td>0.495623</td>\n",
       "      <td>0.002114</td>\n",
       "      <td>0.039113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>0.405767</td>\n",
       "      <td>0.014075</td>\n",
       "      <td>0.223191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.016585</td>\n",
       "      <td>0.230116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rho   p_value     p_adj\n",
       "Pyridoxal hydrochloride  0.550721  0.000501  0.039113\n",
       "Pyruvic acid            -0.532698  0.000824  0.039113\n",
       "Phenylpyruvic acid      -0.517765  0.001220  0.039113\n",
       "Orotic acid              0.510041  0.001484  0.039113\n",
       "4-Pyridoxic acid         0.499743  0.001914  0.039113\n",
       "Creatine                 0.495623  0.002114  0.039113\n",
       "Thymine                  0.405767  0.014075  0.223191\n",
       "5-HIAA                  -0.396756  0.016585  0.230116"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_DTAPHib_correlations_clr = metab_data_clr_v5.apply(spearmanr, b=meta_v5['median_mmNorm_DTAPHib']).transpose()\n",
    "v5_DTAPHib_correlations_clr.columns = ['rho', 'p_value']\n",
    "v5_DTAPHib_correlations_clr['p_adj'] = p_adjust(v5_DTAPHib_correlations_clr['p_value'])\n",
    "v5_DTAPHib_correlations_clr = v5_DTAPHib_correlations_clr.sort_values('p_value')\n",
    "v5_DTAPHib_correlations_clr.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff5061b1-a8ad-4fe3-ac28-4ab79b66ddbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phenylpyruvic acid</th>\n",
       "      <td>-0.658422</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.003452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deoxycytidine 5-triphosphate</th>\n",
       "      <td>0.406083</td>\n",
       "      <td>0.019034</td>\n",
       "      <td>0.514584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>0.404746</td>\n",
       "      <td>0.019470</td>\n",
       "      <td>0.514584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>0.369652</td>\n",
       "      <td>0.034238</td>\n",
       "      <td>0.514584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Sorbose</th>\n",
       "      <td>0.364973</td>\n",
       "      <td>0.036760</td>\n",
       "      <td>0.514584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "Phenylpyruvic acid           -0.658422  0.000031  0.003452\n",
       "Deoxycytidine 5-triphosphate  0.406083  0.019034  0.514584\n",
       "myo-Inositol                  0.404746  0.019470  0.514584\n",
       "L-tryptophan-D5               0.369652  0.034238  0.514584\n",
       "L-Sorbose                     0.364973  0.036760  0.514584"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_clr_v5 = metab_data_clr.loc[meta_PCV_v5.index].transpose()\n",
    "v5_PCV_correlations_clr = metab_data_PCV_clr_v5.transpose().apply(spearmanr, b=meta_PCV_v5['median_mmNorm_PCV']).transpose()\n",
    "v5_PCV_correlations_clr.columns = ['rho', 'p_value']\n",
    "v5_PCV_correlations_clr['p_adj'] = p_adjust(v5_PCV_correlations_clr['p_value'])\n",
    "v5_PCV_correlations_clr = v5_PCV_correlations_clr.sort_values('p_value')\n",
    "v5_PCV_correlations_clr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9d8744-de59-4198-bc94-bbd971ff2152",
   "metadata": {},
   "source": [
    "Same results as all other 2 month correlations for DTAPHib and PCV."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0377bb-0877-4036-a11e-d5e45d4ae158",
   "metadata": {},
   "source": [
    "### 12 month correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9562e912-fbb0-4044-af69-fcc589de5d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.618808</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.559617</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.557653</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.543731</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.527844</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.001285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-3-Dihydroxyisovalerate</th>\n",
       "      <td>-0.502050</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.002763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Sorbose</th>\n",
       "      <td>-0.489494</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.003201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Mannose</th>\n",
       "      <td>-0.489494</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.003201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Xylose</th>\n",
       "      <td>-0.483686</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.003460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Succinic acid</th>\n",
       "      <td>-0.467458</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.004854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myo-Inositol</th>\n",
       "      <td>-0.467117</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>0.004854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylitol</th>\n",
       "      <td>-0.454219</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.006585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Itaconic acid</th>\n",
       "      <td>-0.449778</td>\n",
       "      <td>0.000822</td>\n",
       "      <td>0.006585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oxamic acid</th>\n",
       "      <td>-0.449436</td>\n",
       "      <td>0.000831</td>\n",
       "      <td>0.006585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Hydroxybenzoic acid</th>\n",
       "      <td>-0.445422</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.006929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melibiose</th>\n",
       "      <td>-0.441493</td>\n",
       "      <td>0.001052</td>\n",
       "      <td>0.007185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cis-Aconitic acid</th>\n",
       "      <td>-0.437820</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.007185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trans-Aconitic acid</th>\n",
       "      <td>-0.437820</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.007185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Methyl-2-oxovaleric acid</th>\n",
       "      <td>-0.436112</td>\n",
       "      <td>0.001230</td>\n",
       "      <td>0.007185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>0.423557</td>\n",
       "      <td>0.001755</td>\n",
       "      <td>0.009742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoxyl sulfate-13C6</th>\n",
       "      <td>-0.420823</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>0.010008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate-d7</th>\n",
       "      <td>-0.413478</td>\n",
       "      <td>0.002313</td>\n",
       "      <td>0.011671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Arabinose</th>\n",
       "      <td>-0.384182</td>\n",
       "      <td>0.004926</td>\n",
       "      <td>0.023774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deoxycytidine 5-triphosphate</th>\n",
       "      <td>-0.370174</td>\n",
       "      <td>0.006910</td>\n",
       "      <td>0.030741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxycytidine 5-diphosphate</th>\n",
       "      <td>0.370089</td>\n",
       "      <td>0.006924</td>\n",
       "      <td>0.030741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha-D(+)Mannose 1-phosphate</th>\n",
       "      <td>-0.362402</td>\n",
       "      <td>0.008286</td>\n",
       "      <td>0.035373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.336693</td>\n",
       "      <td>0.014662</td>\n",
       "      <td>0.060276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    rho   p_value     p_adj\n",
       "Guanosine                     -0.618808  0.000001  0.000112\n",
       "L-tryptophan-D5               -0.559617  0.000016  0.000647\n",
       "Inosine                       -0.557653  0.000017  0.000647\n",
       "Adenosine                     -0.543731  0.000031  0.000861\n",
       "serotonin                      0.527844  0.000058  0.001285\n",
       "2-3-Dihydroxyisovalerate      -0.502050  0.000149  0.002763\n",
       "L-Sorbose                     -0.489494  0.000231  0.003201\n",
       "D-Mannose                     -0.489494  0.000231  0.003201\n",
       "D-Xylose                      -0.483686  0.000281  0.003460\n",
       "Succinic acid                 -0.467458  0.000476  0.004854\n",
       "myo-Inositol                  -0.467117  0.000481  0.004854\n",
       "Xylitol                       -0.454219  0.000719  0.006585\n",
       "Itaconic acid                 -0.449778  0.000822  0.006585\n",
       "Oxamic acid                   -0.449436  0.000831  0.006585\n",
       "4-Hydroxybenzoic acid         -0.445422  0.000936  0.006929\n",
       "Melibiose                     -0.441493  0.001052  0.007185\n",
       "cis-Aconitic acid             -0.437820  0.001171  0.007185\n",
       "trans-Aconitic acid           -0.437820  0.001171  0.007185\n",
       "4-Methyl-2-oxovaleric acid    -0.436112  0.001230  0.007185\n",
       "L-Serine                       0.423557  0.001755  0.009742\n",
       "indoxyl sulfate-13C6          -0.420823  0.001893  0.010008\n",
       "p-cresol sulfate-d7           -0.413478  0.002313  0.011671\n",
       "L-Arabinose                   -0.384182  0.004926  0.023774\n",
       "Deoxycytidine 5-triphosphate  -0.370174  0.006910  0.030741\n",
       "2-Deoxycytidine 5-diphosphate  0.370089  0.006924  0.030741\n",
       "alpha-D(+)Mannose 1-phosphate -0.362402  0.008286  0.035373\n",
       "5-HIAA                         0.336693  0.014662  0.060276"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_DTAPHib_correlations_clr = metab_data_clr_v9.apply(spearmanr, b=meta_v9['median_mmNorm_DTAPHib']).transpose()\n",
    "v9_DTAPHib_correlations_clr.columns = ['rho', 'p_value']\n",
    "v9_DTAPHib_correlations_clr['p_adj'] = p_adjust(v9_DTAPHib_correlations_clr['p_value'])\n",
    "v9_DTAPHib_correlations_clr = v9_DTAPHib_correlations_clr.sort_values('p_value')\n",
    "v9_DTAPHib_correlations_clr.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "07a7c9be-034b-4a8e-9755-0d4251fa0100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adenosine</th>\n",
       "      <td>-0.480181</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.021900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.477557</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.021900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.429050</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.062183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.415113</td>\n",
       "      <td>0.002454</td>\n",
       "      <td>0.068096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.392851</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.078341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      rho   p_value     p_adj\n",
       "Adenosine       -0.480181  0.000363  0.021900\n",
       "Guanosine       -0.477557  0.000395  0.021900\n",
       "serotonin        0.429050  0.001681  0.062183\n",
       "Inosine         -0.415113  0.002454  0.068096\n",
       "L-tryptophan-D5 -0.392851  0.004350  0.078341"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_clr_v9 = metab_data_clr.loc[meta_PCV_v9.index].transpose()\n",
    "v9_PCV_correlations_clr = metab_data_PCV_clr_v9.transpose().apply(spearmanr, b=meta_PCV_v9['median_mmNorm_PCV']).transpose()\n",
    "v9_PCV_correlations_clr.columns = ['rho', 'p_value']\n",
    "v9_PCV_correlations_clr['p_adj'] = p_adjust(v9_PCV_correlations_clr['p_value'])\n",
    "v9_PCV_correlations_clr = v9_PCV_correlations_clr.sort_values('p_value')\n",
    "v9_PCV_correlations_clr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6f84b5-f5b3-4d3b-9323-e187909d79db",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b17ecd5-ad35-4b56-b7c8-4a2058e72aad",
   "metadata": {},
   "source": [
    "## Compare normalizations for split titers\n",
    "\n",
    "Build up some venn diagrams to compare normalizations for the split titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dc17a4aa-3338-49a3-a0d8-6a893705a28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shaffmic/miniconda3/envs/imc/lib/python3.9/site-packages/matplotlib_venn/_venn3.py:117: UserWarning: Bad circle positioning\n",
      "  warnings.warn(\"Bad circle positioning\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = venn3([set(v9_DTAPHib_correlations.query('p_adj < .05').index),\n",
    "           set(v9_DTAPHib_correlations_rel.query('p_adj < .05').index),\n",
    "           set(v9_DTAPHib_correlations_clr.query('p_adj < .05').index)], set_labels=('un-normalized',\n",
    "                                                                                     'relative abundance', 'CLR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7b4f1b71-2cee-49bd-84be-30c1aa2fcfb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = venn3([set(v9_PCV_correlations.query('p_adj < .05').index),\n",
    "           set(v9_PCV_correlations_rel.query('p_adj < .05').index),\n",
    "           set(v9_PCV_correlations_clr.query('p_adj < .05').index)], set_labels=('un-normalized',\n",
    "                                                                                     'relative abundance', 'CLR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360f77fa-bcb3-4639-9ab0-224d474aa94d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
